Seeded LDA Whole CS Corpus  exp2 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 781,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lda import guidedlda as glda\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Importing Clean Corpus**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 782,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "clean_corpus=joblib.load(r\"C:\\Users\\LENOVO\\Desktop\\cs_concept.job\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 783,
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib\n",
    "whole_corpus=joblib.load(r\"C:\\Users\\LENOVO\\Desktop\\cs_concept_noisy.job\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 784,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "labeled_corpus=pd.read_csv(r\"C:\\Users\\LENOVO\\Desktop\\cs_gs.csv\")\n",
    "freq=labeled_corpus['freq']\n",
    "terms=labeled_corpus['term']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 785,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gensim\n",
    "from gensim import corpora\n",
    "dictionary=whole_corpus['dict']\n",
    "docs=whole_corpus['docs']\n",
    "doc_term_matrix = [dictionary.doc2bow(rev) for rev in docs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating Document Dictionary and Document-Term Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 786,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 'analysis'], [1, 'artificial'], [2, 'artificial_bee_colony'], [3, 'color_information'], [4, 'cross_validation'], [5, 'explaining'], [6, 'handle'], [7, 'increase'], [8, 'linear_discriminant'], [9, 'method'], [10, 'neural_networks'], [11, 'novel'], [12, 'objective'], [13, 'principal_component'], [14, 'proposed_method'], [15, 'recall_rate'], [16, 'repetition'], [17, 'result'], [18, 'state-of-the-art'], [19, 'support_vector_machines'], [20, 'total'], [21, 'vector_machine'], [22, 'vision'], [23, 'accident'], [24, 'clinical_data'], [25, 'graphic'], [26, 'healthy_subjects'], [27, 'movement'], [28, 'present_study'], [29, 'purpose'], [30, 'quantify'], [31, 'smoothness'], [32, 'subject'], [33, 'subjects'], [34, 'viewing'], [35, 'virtual'], [36, 'amounts'], [37, 'assists'], [38, 'data_entry'], [39, 'example'], [40, 'export'], [41, 'extraction'], [42, 'implement'], [43, 'input'], [44, 'limitation'], [45, 'link'], [46, 'management_system'], [47, 'moving'], [48, 'occupancy'], [49, 'operating'], [50, 'outside'], [51, 'package'], [52, 'practitioner'], [53, 'processing'], [54, 'record'], [55, 'researcher'], [56, 'spatial'], [57, 'spatial_distribution'], [58, 'tags'], [59, 'toolbox'], [60, '2016'], [61, 'catchment'], [62, 'characteristic'], [63, 'conservation'], [64, 'copyright'], [65, 'cross'], [66, 'discover'], [67, 'entropy'], [68, 'geology'], [69, 'groundwater'], [70, 'guide'], [71, 'heterogeneous_environment'], [72, 'john_wiley'], [73, 'land_use'], [74, 'learning_approach'], [75, 'ltd.'], [76, 'machine'], [77, 'maxima'], [78, 'modify'], [79, 'occurrence'], [80, 'percentage'], [81, 'prediction_accuracy'], [82, 'sample_size'], [83, 'sons'], [84, 'stream'], [85, 'suitability'], [86, 'variable'], [87, 'advantage'], [88, 'analyse'], [89, 'common_data_structure'], [90, 'data_source'], [91, 'facilitates'], [92, 'growing_number'], [93, 'organisms'], [94, 'phylogenetics'], [95, 'phylogenies'], [96, 'present'], [97, 'rich_set'], [98, 'uses'], [99, 'bitcoin'], [100, 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'thread-level_parallelism'], [7872, 'farash'], [7873, 'applications_tool'], [7874, 'avispa'], [7875, 'formal_security_analysis'], [7876, 'communication_system_security'], [7877, 'disclosure'], [7878, 'underlying_idea'], [7879, '3d_technologies'], [7880, 'fibre'], [7881, 'structured_documents'], [7882, 'molecular_dynamics_simulations'], [7883, 'tcam'], [7884, 'server_side'], [7885, 'plaque'], [7886, 'potential_threats'], [7887, 'stata'], [7888, 'avionics'], [7889, 'interaction_device'], [7890, 'analogue'], [7891, 'ntru'], [7892, 'user_input'], [7893, 'current_effort'], [7894, 'unified_model'], [7895, 'phase_ii'], [7896, 'zigbee'], [7897, 'smart_mobile_device'], [7898, 'core_idea'], [7899, 'smartphones'], [7900, 'research_team'], [7901, 'customer_satisfaction'], [7902, 'cybernetics'], [7903, 'software_cybernetics'], [7904, 'harmony'], [7905, 'sdns'], [7906, 'km_practices'], [7907, 'computer-aided_software_engineering_tool'], [7908, 'main_area'], [7909, 'proper_choice'], [7910, 'stochastic_nature'], [7911, 'proof'], [7912, 'mathematical_modeling'], [7913, 'software_engineering_measures'], [7914, 'tcp/ip'], [7915, 'interactive_model'], [7916, 'cache_replacement_policy'], [7917, 'mechatronic_system'], [7918, 'increased_number'], [7919, 'sandy_bridge'], [7920, 'predictive_power'], [7921, 'wide_class'], [7922, 'grass_gis'], [7923, 'scientific_field'], [7924, 'affinity_matrix'], [7925, 'quaternion'], [7926, 'substation_automation'], [7927, 'visual_vocabulary'], [7928, 'weka'], [7929, 'water_quality'], [7930, 'phase_field'], [7931, 'cyberinfrastructure'], [7932, 'hindi'], [7933, 'artefact'], [7934, 'computer_skills'], [7935, 'semi-supervised_classification'], [7936, 'vein'], [7937, 'active_system'], [7938, 'subgroup'], [7939, 'burrows-wheeler'], [7940, 'suffix_array_construction_algorithm'], [7941, 'theta'], [7942, 'correct_classification_rate'], [7943, 'cost_optimization'], [7944, 'r-tree'], [7945, 'sublinear'], [7946, 'maximum_diameter'], [7947, 'surface_mesh'], [7948, 'field_surveys'], [7949, 'dramatic_growth'], [7950, 'coloring_algorithm'], [7951, 'kuhn'], [7952, 'brain_project'], [7953, 'global_memory'], [7954, 'task_scheduling_algorithm'], [7955, 'k-12_classrooms'], [7956, 'distributed_classification'], [7957, 'efficient_computation'], [7958, 'tmis'], [7959, 'terrestrial_laser_scanning'], [7960, 'description_method'], [7961, 'grid_cells'], [7962, 'canada'], [7963, 'meteorological_data'], [7964, 'portable_device'], [7965, 'large-eddy_simulation'], [7966, 'traffic_engineering'], [7967, 'electromagnetic_wave'], [7968, 'iccg_method'], [7969, 'labview'], [7970, 'remote_access'], [7971, 'mahalanobis_distance'], [7972, 'lego_mindstorms'], [7973, 'openacc'], [7974, 'history'], [7975, 'user-friendly_graphical_interface'], [7976, 'transmitted_data'], [7977, 'interrelation'], [7978, 'interconnection'], [7979, 'distributed_computing_networks'], [7980, 'irregular_objects'], [7981, 'algebraic_operations'], [7982, 'upper_and_lower_bounds'], [7983, 'atlas_experiment'], [7984, 'catalogue'], [7985, 'eventindex'], [7986, 'forward_operator'], [7987, 'multi-core_architecture'], [7988, 'biologists'], [7989, 'computer_vision_community'], [7990, 'multiuser_interference'], [7991, 'deep_analysis'], [7992, 'belle_ii_experiment'], [7993, 'annihilation'], [7994, 'institute'], [7995, 'common_divisor'], [7996, 'vocational_college'], [7997, 'clearance'], [7998, 'museums'], [7999, 'average'], [8000, 'explosive_hazard_detection'], [8001, 'explosive_hazards'], [8002, 'distribution_parameter'], [8003, 'plastic'], [8004, 'nonlinear_dynamical_systems'], [8005, 'circle'], [8006, 'complete_binary_tree'], [8007, 'mobius'], [8008, 'faulty_set'], [8009, 'object_properties'], [8010, 'image_manipulation'], [8011, 'ancestor'], [8012, 'np-hard_problems'], [8013, 'planar_graphs'], [8014, 'system_behavior'], [8015, 'core_aspect'], [8016, 'qt_framework'], [8017, 'kinetic_energy'], [8018, 'debate'], [8019, 'distributed_representation'], [8020, 'distributed_optimization'], [8021, 'food_safety'], [8022, 'categorical_data'], [8023, 'system_operator'], [8024, 'adaptive_software'], [8025, 'kids'], [8026, 'accommodation'], [8027, 'border'], [8028, 'influenza'], [8029, 'kinect_sensor'], [8030, 'proposed_device'], [8031, 'proposed_pipeline'], [8032, 'multi-server_architecture'], [8033, 'demand_response'], [8034, 'pump'], [8035, 'emerging_use'], [8036, 'current_technology'], [8037, 'shared_memory_system'], [8038, 'failure_detector'], [8039, 'posture'], [8040, 'switzerland'], [8041, 'heliostat'], [8042, 'heliostat_field'], [8043, 'heliostats'], [8044, 'fptas'], [8045, 'operation_time'], [8046, 'smart_cities'], [8047, 'triangulated_surfaces'], [8048, 'null_value'], [8049, 'clothing'], [8050, 'traditional_implementation'], [8051, 'ads-b'], [8052, 'specialty'], [8053, 'binary_decision_diagram'], [8054, 'code.org'], [8055, 'flame'], [8056, 'ignition'], [8057, 'linked_open_data'], [8058, 'rational_unified_process'], [8059, 'reallocation'], [8060, 'cent'], [8061, 'transponder'], [8062, 'nearest_neighbor_algorithm'], [8063, 'searchable_encryption'], [8064, 'research_group'], [8065, 'real_world_data'], [8066, 'computer_data'], [8067, 'safe_operation'], [8068, 'hu_moments'], [8069, 'genus'], [8070, 'tremendous_growth'], [8071, 'connected_subgraph'], [8072, 'faulty_element'], [8073, 'k-ary_n-cube'], [8074, 'structure_fault'], [8075, 'distributed_manner'], [8076, 'ls-svm'], [8077, 'convergence_analysis'], [8078, 'legendre'], [8079, 'problem_data'], [8080, 'lbp_codes'], [8081, 'cell_mapping_methods'], [8082, 'characterization'], [8083, 'coatings'], [8084, 'glcm'], [8085, 'substantial_improvements'], [8086, 'textile'], [8087, 'transient_faults'], [8088, 'message'], [8089, 'fluorescence'], [8090, 'arp_spoofing_attack'], [8091, 'trajectory'], [8092, 'e._coli'], [8093, 'potential_benefit'], [8094, 'increasing_trend'], [8095, 'mapping_process'], [8096, 'processing_nodes'], [8097, 'idps'], [8098, 'unified_modeling_language'], [8099, 'learning_levels'], [8100, 'solar'], [8101, 'poset'], [8102, 'information_modeling'], [8103, 'soil-structure_interaction'], [8104, 'vehicle_tracking'], [8105, 'forging'], [8106, 'subject_domain'], [8107, 'electric_energy'], [8108, 'powertrain_system'], [8109, 'bankruptcy'], [8110, 'adjacency'], [8111, 'brevity'], [8112, 'digital_topology'], [8113, 'lung_cancer'], [8114, 'marker'], [8115, 'computational_effort'], [8116, 'gray_level'], [8117, 'biomass'], [8118, 'volatile_memory'], [8119, 'comput_appl_eng_educ_24'], [8120, 'distraction'], [8121, 'surgery'], [8122, 'supporting_point'], [8123, 'tunnelling'], [8124, 'parallel_mode'], [8125, 'real-time_simulation'], [8126, 'quick_response'], [8127, 'linearizability'], [8128, 'code_reusability'], [8129, 'visual_content'], [8130, 'primary_goal'], [8131, 'particular_attention'], [8132, 'equilibrium_computation'], [8133, 'ban_logic'], [8134, 'pole'], [8135, 'android_os'], [8136, 'checker'], [8137, 'vacuum'], [8138, 'departure'], [8139, 'critical_point'], [8140, 'eruption'], [8141, 'final_state'], [8142, 'real_darknet_traffic'], [8143, 'investigations'], [8144, 'archived_data'], [8145, 'benchmark_programs'], [8146, 'program_transformation'], [8147, 'halves'], [8148, 'basic_characteristics'], [8149, 'non-destructive_testing'], [8150, 'trip'], [8151, 'machine_vision_system'], [8152, 'scanning_scheme'], [8153, 'recipe'], [8154, 'mobile_data_collection_applications'], [8155, 'mobile_service'], [8156, 'technical_issues'], [8157, 'online_survey'], [8158, 'kenya'], [8159, 'mobile_usability'], [8160, 'nucleation'], [8161, 'comput_appl_eng_educ_23'], [8162, 'emulation'], [8163, 'lisa'], [8164, 'asynchronous_message-passing_system'], [8165, 'galerkin'], [8166, 'core_element'], [8167, 'cellular_networks'], [8168, 'distributed_algorithm_design'], [8169, 'step'], [8170, 'power_control'], [8171, 'inventory_control'], [8172, 'expert_judgment'], [8173, 'tiling'], [8174, 'portfolio_solver'], [8175, 'dns_service'], [8176, 'connector'], [8177, 'building_information_models'], [8178, 'laboratory_exercises'], [8179, 'gnu_general_public_license'], [8180, 'e-commerce'], [8181, 'embedded_security'], [8182, 'postgraduate_students'], [8183, 'self-efficacy'], [8184, 'wearable_technology'], [8185, 'security_investments'], [8186, 'aec_curricula'], [8187, 'unlabeled_samples'], [8188, 'coordination'], [8189, 'local_search_procedure'], [8190, 'cbse'], [8191, 'set_s'], [8192, 'space-filling_curve'], [8193, 'commonalities'], [8194, 'discrete_system'], [8195, 'opendss'], [8196, 'content-based_image_retrieval'], [8197, 'r2rml'], [8198, 'channel_attack'], [8199, 'solar_orbiter'], [8200, 'atlas'], [8201, 'viable_alternative'], [8202, 'blank'], [8203, 'trace_table'], [8204, 'rule-based_approach'], [8205, 'stripe'], [8206, 'bit_key_length'], [8207, 'derived_correlation'], [8208, 'dialog'], [8209, 'gate_equivalents'], [8210, 'oracle_inequality'], [8211, 'thermograms'], [8212, 'sequence'], [8213, 'tsunami_wave'], [8214, 'vane'], [8215, 'lotka-volterra'], [8216, 'self-calibration'], [8217, 'transit'], [8218, 'terahertz'], [8219, 'guis'], [8220, 'aforementioned_problem'], [8221, 'quantum_secret_sharing'], [8222, 'antiport_rules'], [8223, 'symport/antiport_rule'], [8224, 'symport_rules'], [8225, 'tissue_p_systems'], [8226, 'memif'], [8227, 'software_management'], [8228, 'ders'], [8229, 'shopping'], [8230, 'fundamental_importance'], [8231, 'illness'], [8232, 'pc_cluster'], [8233, 'warning_system'], [8234, 'surgical_training'], [8235, 'isosurfaces'], [8236, 'stochastic_programming'], [8237, 'corona'], [8238, 'singular_integral'], [8239, 'approximation_factor'], [8240, 'cs_plugged'], [8241, 'cs_unplugged'], [8242, 'teleportation'], [8243, 'kd-trees'], [8244, 'volume_hierarchies'], [8245, 'printing_technology'], [8246, 'polarization'], [8247, 'entanglement_concentration'], [8248, 'waveguides'], [8249, 'cmes'], [8250, 'albedo'], [8251, 'hw/sw'], [8252, 'pelc'], [8253, 'xpath'], [8254, 'iaas'], [8255, 'z-wave'], [8256, 'makd'], [8257, 'simit'], [8258, 'memory_contents'], [8259, 'computer_network_technology'], [8260, 'word_clouds']]\n"
     ]
    }
   ],
   "source": [
    "id2word = [[i,dictionary[i]] for i in range(len(dictionary))]\n",
    "print(id2word)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 787,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtm=[]\n",
    "for i in range(len(docs)):\n",
    "  doc_counts=[]\n",
    "  for j in range(len(dictionary)):\n",
    "    if (dictionary[j] in docs[i]):\n",
    "      doc_counts.append(1)\n",
    "    else:\n",
    "      doc_counts.append(0)\n",
    "  dtm.append(doc_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 788,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5746, 8261)\n",
      "102103\n"
     ]
    }
   ],
   "source": [
    "dtm=np.array(dtm)\n",
    "print(dtm.shape)\n",
    "print(dtm.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 789,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8261\n",
      "5746\n"
     ]
    }
   ],
   "source": [
    "print(len(dictionary))\n",
    "print(len(docs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Computing TF-IDF of Core Concepts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 790,
   "metadata": {},
   "outputs": [],
   "source": [
    "terms_list=[]\n",
    "for term in terms:\n",
    "    terms_list.append(term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 791,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "for term in terms_list:\n",
    "    if term=='artificial_bee_colony':\n",
    "        print('yes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 792,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('analysis', 'artificial', 'artificial_bee_colony', 'color_information', 'cross_validation', 'explaining', 'handle', 'increase', 'linear_discriminant', 'method', 'neural_networks', 'novel', 'objective', 'principal_component', 'proposed_method', 'recall_rate', 'repetition', 'result', 'state-of-the-art', 'support_vector_machines', 'total', 'vector_machine', 'vision', 'accident', 'clinical_data', 'graphic', 'healthy_subjects', 'movement', 'present_study', 'purpose', 'quantify', 'smoothness', 'subject', 'subjects', 'viewing', 'virtual', 'amounts', 'assists', 'data_entry', 'example', 'export', 'extraction', 'implement', 'input', 'limitation', 'link', 'management_system', 'moving', 'occupancy', 'operating', 'outside', 'package', 'practitioner', 'processing', 'record', 'researcher', 'spatial', 'spatial_distribution', 'tags', 'toolbox', '2016', 'catchment', 'characteristic', 'conservation', 'copyright', 'cross', 'discover', 'entropy', 'geology', 'groundwater', 'guide', 'heterogeneous_environment', 'john_wiley', 'land_use', 'learning_approach', 'ltd.', 'machine', 'maxima', 'modify', 'occurrence', 'percentage', 'prediction_accuracy', 'sample_size', 'sons', 'stream', 'suitability', 'variable', 'advantage', 'analyse', 'common_data_structure', 'data_source', 'facilitates', 'growing_number', 'organisms', 'phylogenetics', 'phylogenies', 'present', 'rich_set', 'uses', 'bitcoin', 'cause', 'create', 'existing_methods', 'fundamental_question', 'future', 'lower_cost', 'mean', 'mechanism', 'mining', 'network_operation', 'operation', 'price', 'quantity', 'rate', 'reduces', 'reward', 'running', 'securing', 'distribution', 'gamma', 'image_processing', 'measure', 'metal', 'probe', 'sheet', 'technique', 'transition', 'consists', 'image_information', 'manufacture', 'practicality', 'product', 'product_design', 'research_field', 'visualization_technology', 'csem', 'divide_and_conquer_approach', 'effective', 'finite_difference', 'finite_element', 'major_advantage', 'major_hurdle', 'outer_region', 'parallel', 'parallel_scalability', 'produce', '3d_reconstruction', '3d_shape', 'diverse_set', 'elsevier_inc._all_rights', 'experimental_result', 'inaccuracy', 'pose', 'prior', 'prior_model', 'propose', 'scene', 'single_image', 'test', 'based', 'clustering_technique', 'dual', 'element', 'engineering', 'fundamental_process', 'fuzzy', 'matching', 'outperforms', 'patch', 'reverse', 'term', 'unsupervised_clustering', 'assess', 'benefit', 'compare', 'computer', 'conduct', 'e.g.', 'geometric_distortion', 'lighting_conditions', 'opinion', 'screen', 'snapshot', 'visual_quality_assessment', 'watermarking', '3d_model', 'advancement', 'based_technique', 'construct', 'data', 'detail', 'disadvantage', 'feasible', 'format', 'image', 'model', 'modern_computer', 'outcome', 'point_cloud', 'printer', 'reconstruction', 'reverse_engineering', 'design_model', 'gesture', 'hand_gesture', 'navigating', 'recognition', 'user', 'user_interaction', 'user_interface', 'ability', 'benchmark', 'extend', 'participant', 'retrieval_algorithm', 'shape_retrieval', 'training_data', 'training_set', 'adaptation', 'consideration', 'dice', 'irregularity', 'potential', 'proposal', 'sample_data', '2015', 'aims', 'battery_life', 'bottleneck', 'delegation', 'demonstrates', 'desirable_properties', 'discrete_logarithm', 'mobile_network', 'mobile_user', 'offer', 'original_signer', 'period', 'proxy_signature', 'proxy_signer', 'signature_scheme', 'signing', 'warrant', 'applicability', 'beta', 'constant_number', 'constructing', 'efficient_algorithm', 'epsilon', 'exhibit', 'fundamental_problem', 'graph_algorithm', 'notion', 'numerous_applications', 'pair', 'publication', 'resource', 'setting', 'shortest_path', 'vast_range', 'vertex', 'exploit', 'lifetime', 'low_power', 'machine-learning', 'many-core', 'meeting', 'performance_gain', 'performance_requirement', 'small-world', 'computational', 'defense', 'multidimensional_data', 'relationship', 'return', 'spatial_data_structure', 'traffic', 'utility', 'abstract_state_machine', 'capture', 'characterizing', 'class', 'defines', 'elsevier_b.v._all_rights', 'essence', 'extension', 'machine_model', 'satisfies', 'sequential', 'update', 'additive_white_gaussian_noise', 'adversary', 'embedding', 'fact', 'filtering', 'peak', 'proposed_technique', 'robust', 'spatial_domain', 'watermark', 'watermarked_image', 'watermarking_scheme', 'worst_case', 'distance_function', 'euclidean', 'furthermore', 'local_structure', 'machine_learning_algorithm', 'manifold_learning', 'neighborhood', 'partial', 'query_point', 'reference', 'respect', 'algorithm', 'assignment', 'clause', 'complexity', 'local', 'polynomial_time', 'search', 'search_algorithm', 'turn', '2017', 'advantages', 'b._v.', 'defect', 'detection_algorithm', 'detection_methods', 'deviation', 'elsevier', 'human_operator', 'online', 'presence', 'radii', 'sample', 'shape', 'shape_model', 'underlying_concept', 'validation', 'vision_system', 'x-ray', 'adding', 'common_way', 'creating', 'cs_education', 'judges', 'learner', 'piece', 'voting', 'bound', 'complement', 'connected_component', 'data_structures', 'et_al.', 'implementing', 'performing', 'recursive', 'cloud_data', 'experiments', 'fast', 'finally', 'fitting', 'generated_model', 'gradient', 'kinds', 'lesion', 'minute', 'requirement', 'visualization_method', 'acceptance', 'assumption', 'black-box', 'complex_model', 'complex_task', 'decision-makers', 'decision_support', 'elsevier_ltd._all_rights', 'explanation', 'flexibility', 'incentive', 'inside', 'learning', 'machine_learning_model', 'novel_use', 'outline', 'possibility', 'prediction_model', 'recommendation', 'sale', 'separation', 'significant_advantage', 'significant_benefit', 'users', 'algorithms', 'attribute', 'capability', 'existing_knowledge', 'foci', 'information', 'knowledge', 'language', 'paper_presents', 'popularization', 'relational', 'series', 'single_type', 'cmos', 'creative_commons_attribution', 'dynamic_range', 'gain', 'imaging', 'raspberry', 'real-world', 'reproduction', 'sensing', 'spectral_response', 'spie', 'the_raspberry_pi', 'account', 'average_time', 'classification_method', 'current', 'disturbance', 'fire_detection', 'kalman', 'nozzle', 'resulting', 'sampling', 'segmenting', 'standard_deviation', 'suppression', 'atmosphere', 'cover', 'impact', 'minimizes', 'simple', 'time_consuming', 'algorithm_design', 'configuration', 'monitoring_system', 'proposed_model', 'risk', 'risk_management', 'scoring', 'system_design', 'correspondence', 'define', 'hamming', 'iterative_approach', 'optimal_solution', 'option', 'relation', 'representative', 'restriction', 'aspect', 'averaging', 'classification_model', 'cross-validation', 'dimensionality_reduction', 'elsevier_b.v.', 'feature_subset', 'predictive_performance', 'split', 'test_set', 'unsupervised_learning', 'data_fusion', 'duplicate', 'experimental', 'order_relation', 'priori', 'tuple', 'development_methodology', 'development_process', 'maturity', 'multi-agent', 'organization', 'software', 'tuning', 'usefulness', 'accurate_prediction', 'based_algorithm', 'computer_vision', 'damage_detection', 'identifying', 'improvement', 'integrating', 'laser', 'measurement', 'noise_level', 'non-contact', 'optical_flow', 'relative', 'skew', 'stiffness', 'structural', 'structural_damage', 'structural_properties', 'system_identification', 'video', 'attacker', 'attempt', 'evaluation_result', 'improved_method', 'incoming_packet', 'intend', 'internet', 'internet_users', 'malicious_activities', 'malicious_packets', 'multiple_attributes', 'sender', 'spikes', 'tactics', 'computer_program', 'database_management', 'examples', 'multiple_tables', 'records', 'design_approach', 'execution', 'fluent', 'fluid_dynamics', 'instance', 'multi-objective_optimization_problem', 'non-dominated_sorting_genetic_algorithm', 'nsga-ii', 'antenna', 'lead', 'main_reason', 'operating_systems', 'technical_capabilities', 'technical_characteristics', 'boundary_condition', 'core', 'discrete', 'flow_regime', 'guo_et_al.', 'licensing', 'magnitude', 'nature', 'open_source', 'openfoam', 'parallel_computing', 'physical_space', 'program', 'restrictions', 'solution_method', 'symmetric', 'test_case', 'wall', 'basic_idea', 'convergence', 'decomposition_method', 'numerical_experiments', 'solving', 'ddos', 'detecting', 'distributed_denial_of_service', 'flash', 'information_distance', 'paper_investigates', 'shannon_entropy', 'automation', 'computer-vision', 'gravity', 'open-source', 'opencv', 'conjunction', 'modified_version', 'parallel_implementation', 'square', 'domain', 'fdtd', 'computed_tomography', 'crack_initiation', 'crack_propagation', 'durability', 'fuel', 'morphological_features', 'situ', 'thinning', 'trend', 'avoidance', 'challenge', 'closed-loop', 'current_state', 'derived_features', 'dictionary_learning', 'network_connectivity', 'novel_approach', 'scientific_research', 'signal_processing', 'software_design', 'spike_sorting', 'time-frequency_analysis', 'vary', 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'classroom_teaching', 'confidence_level', 'peer', 'programming_and_programming_languages', 'system_error', 'wavefront', 'needle', 'simplex', 'special_focus', 'asphalt', 'fringe', 'galaxy', 'redshifts', 'contour_lines', 'light_detection', 'pevs', 'hamiltonian', 'system_size', 'online_tool', 'physical_memory', 'time_window', 'symbian', 'portfolio', 'pivotal_role', 'wind_power', 'protein_structure', 'lower_frequency', 'geometrical_characteristics', 'spacing', 'provenance', 'technical_disciplines', 'computer-aided_software_engineering', 'functional_requirements', 'probabilistic_inference', 'additional_time', 'certain_class', 'vegetables', 'interactive_projection', 'speedup_factor', 'cavities', 'topological_properties', 'solvent', 'certain_amount', 'multiple_threads', 'secondary_structure', 'tumour', 'human_computer_interaction', 'information_integration', 'bindings', 'certificate_revocation_lists', 'equivalent_key', 'key_recovery_attack', 'cfrp', 'laplacian_eigenmap', 'design/methodology/approach', 'gbed', 'head-mounted_display', 'originality/value', 'research_limitations/implications', 'social_implications', 'visual_reality', 'codification', 'readiness', 'practical_value', 'qos_constraints', 'antarctica', 'electronics', 'purpose_the', 'teleoperation', 'curriculum', 'catalog', 'query_answering', 'semantic_relationship', 'stackoverflow', 'smartphone_security', 'diagnostic_accuracy', 'pretreatment', 'dementia', 'city-scale', 'fisher', 'manufacturer', 'geometric_data_structures', 'individual_system', 'privacy_amplification', 'photon_number_splitting', 'qubits', 'multitask', 'stacking', 'cryptographic_module', 'inference_algorithm', 'data_repositories', 'turbidity', 'shrinkage', 'additional_comments', 'chemical_reactions', 'hydrogen', 'object-oriented_program', 'stitching', 'construction_site', 'failing', 'occlusion', 'sensing_technologies', 'simulation_technology', 'level', 'scientific_visualization', 'lasso', 'fundamental_mechanism', 'huge_computation', 'instructional_intervention', 'smartphone_platform', 'catch', 'representative_set', 'software_engineering_domain', 'query_relaxation', 'theoretical_properties', 'triple_patterns', 'verifiable_secret_sharing', 'parameter_study_job', 'discriminant_feature', 'sqlite', 'client_side', 'integer_factorization_problem', 'lookup_table', 'multi-threading', 'desired_feature', 'threshold_scheme', 'visual_secret_sharing', 'huge_number', \"wei_et_al.'s\", 'cloud_server', 'street', 'mating', 'heterogeneous_parts', 'rectangular_dissections', 'integral_representation', 'illusion', 'industrial_project', 'refactoring_sequence', 'art_algorithm', 'role-playing_game', 'opensees', 'mathematical_programming', 'regression_testing', 'moea/d', 'relevant_literature', 'database_content', 'scalable_application', 'alloy', 'architectural_style', 'scalable_system', 'soap', 'spline', 'nanotechnology', 'posing', 'software_repository', 'scripting_language', 'virtual_platform', 'histogram_modification', 'reversibility', 'ancilla', 'reversible_logic', 'public-key_encryption', 'iran', 'orthopedic_surgeons', 'assembles', 'chinese_characters', 'prey', 'bogie', 'moving_train', 'rolling_stock_examination', 'real-time_os', 'single_table', 'essential_role', 'simplified_version', 'safety', 'superpixel_level', 'salmon', 'companies', 'money', 'text_detection', 'cumulants', 'resource_provider', 'configures', 'restart', 'constructive_method', 'digital_fabrication', 'f-measure', 'study_group', 'weight_value', 'future_evolution', 'sdn_architecture', 'interactive_simulation', 'cryptography_algorithms', 'iot_devices', 'symmetric_key_cryptography', 'affinity', 'business_services', 'critical_analysis', 'existing_approach', 'garment', 'text-mining', 'valence', 'thread-level_parallelism', 'farash', 'applications_tool', 'avispa', 'formal_security_analysis', 'communication_system_security', 'disclosure', 'underlying_idea', '3d_technologies', 'fibre', 'structured_documents', 'molecular_dynamics_simulations', 'tcam', 'server_side', 'plaque', 'potential_threats', 'stata', 'avionics', 'interaction_device', 'analogue', 'ntru', 'user_input', 'current_effort', 'unified_model', 'phase_ii', 'zigbee', 'smart_mobile_device', 'core_idea', 'smartphones', 'research_team', 'customer_satisfaction', 'cybernetics', 'software_cybernetics', 'harmony', 'sdns', 'km_practices', 'computer-aided_software_engineering_tool', 'main_area', 'proper_choice', 'stochastic_nature', 'proof', 'mathematical_modeling', 'software_engineering_measures', 'tcp/ip', 'interactive_model', 'cache_replacement_policy', 'mechatronic_system', 'increased_number', 'sandy_bridge', 'predictive_power', 'wide_class', 'grass_gis', 'scientific_field', 'affinity_matrix', 'quaternion', 'substation_automation', 'visual_vocabulary', 'weka', 'water_quality', 'phase_field', 'cyberinfrastructure', 'hindi', 'artefact', 'computer_skills', 'semi-supervised_classification', 'vein', 'active_system', 'subgroup', 'burrows-wheeler', 'suffix_array_construction_algorithm', 'theta', 'correct_classification_rate', 'cost_optimization', 'r-tree', 'sublinear', 'maximum_diameter', 'surface_mesh', 'field_surveys', 'dramatic_growth', 'coloring_algorithm', 'kuhn', 'brain_project', 'global_memory', 'task_scheduling_algorithm', 'k-12_classrooms', 'distributed_classification', 'efficient_computation', 'tmis', 'terrestrial_laser_scanning', 'description_method', 'grid_cells', 'canada', 'meteorological_data', 'portable_device', 'large-eddy_simulation', 'traffic_engineering', 'electromagnetic_wave', 'iccg_method', 'labview', 'remote_access', 'mahalanobis_distance', 'lego_mindstorms', 'openacc', 'history', 'user-friendly_graphical_interface', 'transmitted_data', 'interrelation', 'interconnection', 'distributed_computing_networks', 'irregular_objects', 'algebraic_operations', 'upper_and_lower_bounds', 'atlas_experiment', 'catalogue', 'eventindex', 'forward_operator', 'multi-core_architecture', 'biologists', 'computer_vision_community', 'multiuser_interference', 'deep_analysis', 'belle_ii_experiment', 'annihilation', 'institute', 'common_divisor', 'vocational_college', 'clearance', 'museums', 'average', 'explosive_hazard_detection', 'explosive_hazards', 'distribution_parameter', 'plastic', 'nonlinear_dynamical_systems', 'circle', 'complete_binary_tree', 'mobius', 'faulty_set', 'object_properties', 'image_manipulation', 'ancestor', 'np-hard_problems', 'planar_graphs', 'system_behavior', 'core_aspect', 'qt_framework', 'kinetic_energy', 'debate', 'distributed_representation', 'distributed_optimization', 'food_safety', 'categorical_data', 'system_operator', 'adaptive_software', 'kids', 'accommodation', 'border', 'influenza', 'kinect_sensor', 'proposed_device', 'proposed_pipeline', 'multi-server_architecture', 'demand_response', 'pump', 'emerging_use', 'current_technology', 'shared_memory_system', 'failure_detector', 'posture', 'switzerland', 'heliostat', 'heliostat_field', 'heliostats', 'fptas', 'operation_time', 'smart_cities', 'triangulated_surfaces', 'null_value', 'clothing', 'traditional_implementation', 'ads-b', 'specialty', 'binary_decision_diagram', 'code.org', 'flame', 'ignition', 'linked_open_data', 'rational_unified_process', 'reallocation', 'cent', 'transponder', 'nearest_neighbor_algorithm', 'searchable_encryption', 'research_group', 'real_world_data', 'computer_data', 'safe_operation', 'hu_moments', 'genus', 'tremendous_growth', 'connected_subgraph', 'faulty_element', 'k-ary_n-cube', 'structure_fault', 'distributed_manner', 'ls-svm', 'convergence_analysis', 'legendre', 'problem_data', 'lbp_codes', 'cell_mapping_methods', 'characterization', 'coatings', 'glcm', 'substantial_improvements', 'textile', 'transient_faults', 'message', 'fluorescence', 'arp_spoofing_attack', 'trajectory', 'e._coli', 'potential_benefit', 'increasing_trend', 'mapping_process', 'processing_nodes', 'idps', 'unified_modeling_language', 'learning_levels', 'solar', 'poset', 'information_modeling', 'soil-structure_interaction', 'vehicle_tracking', 'forging', 'subject_domain', 'electric_energy', 'powertrain_system', 'bankruptcy', 'adjacency', 'brevity', 'digital_topology', 'lung_cancer', 'marker', 'computational_effort', 'gray_level', 'biomass', 'volatile_memory', 'comput_appl_eng_educ_24', 'distraction', 'surgery', 'supporting_point', 'tunnelling', 'parallel_mode', 'real-time_simulation', 'quick_response', 'linearizability', 'code_reusability', 'visual_content', 'primary_goal', 'particular_attention', 'equilibrium_computation', 'ban_logic', 'pole', 'android_os', 'checker', 'vacuum', 'departure', 'critical_point', 'eruption', 'final_state', 'real_darknet_traffic', 'investigations', 'archived_data', 'benchmark_programs', 'program_transformation', 'halves', 'basic_characteristics', 'non-destructive_testing', 'trip', 'machine_vision_system', 'scanning_scheme', 'recipe', 'mobile_data_collection_applications', 'mobile_service', 'technical_issues', 'online_survey', 'kenya', 'mobile_usability', 'nucleation', 'comput_appl_eng_educ_23', 'emulation', 'lisa', 'asynchronous_message-passing_system', 'galerkin', 'core_element', 'cellular_networks', 'distributed_algorithm_design', 'step', 'power_control', 'inventory_control', 'expert_judgment', 'tiling', 'portfolio_solver', 'dns_service', 'connector', 'building_information_models', 'laboratory_exercises', 'gnu_general_public_license', 'e-commerce', 'embedded_security', 'postgraduate_students', 'self-efficacy', 'wearable_technology', 'security_investments', 'aec_curricula', 'unlabeled_samples', 'coordination', 'local_search_procedure', 'cbse', 'set_s', 'space-filling_curve', 'commonalities', 'discrete_system', 'opendss', 'content-based_image_retrieval', 'r2rml', 'channel_attack', 'solar_orbiter', 'atlas', 'viable_alternative', 'blank', 'trace_table', 'rule-based_approach', 'stripe', 'bit_key_length', 'derived_correlation', 'dialog', 'gate_equivalents', 'oracle_inequality', 'thermograms', 'sequence', 'tsunami_wave', 'vane', 'lotka-volterra', 'self-calibration', 'transit', 'terahertz', 'guis', 'aforementioned_problem', 'quantum_secret_sharing', 'antiport_rules', 'symport/antiport_rule', 'symport_rules', 'tissue_p_systems', 'memif', 'software_management', 'ders', 'shopping', 'fundamental_importance', 'illness', 'pc_cluster', 'warning_system', 'surgical_training', 'isosurfaces', 'stochastic_programming', 'corona', 'singular_integral', 'approximation_factor', 'cs_plugged', 'cs_unplugged', 'teleportation', 'kd-trees', 'volume_hierarchies', 'printing_technology', 'polarization', 'entanglement_concentration', 'waveguides', 'cmes', 'albedo', 'hw/sw', 'pelc', 'xpath', 'iaas', 'z-wave', 'makd', 'simit', 'memory_contents', 'computer_network_technology', 'word_clouds')\n"
     ]
    }
   ],
   "source": [
    "vocab=tuple(dictionary[i] for i in range(len(dictionary)))\n",
    "print(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 793,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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1.391114877133519, 0.6955574385667594, 1.0433361578501392, 0.8694467982084494, 0.6955574385667594, 1.0433361578501392, 1.0433361578501392, 1.0433361578501392, 1.7388935964168988, 1.0433361578501392, 1.391114877133519, 0.9274099180890126, 0.6955574385667594, 1.7388935964168988, 1.391114877133519, 1.7388935964168988, 1.391114877133519, 0.6955574385667594, 1.391114877133519, 0.9274099180890126, 0.6955574385667594, 1.391114877133519, 1.391114877133519, 2.0866723157002784, 1.391114877133519, 2.782229754267038, 4.868902069967317, 0.6955574385667594, 1.0433361578501392, 0.6955574385667594, 0.6955574385667594, 1.0433361578501392, 1.0433361578501392, 1.7388935964168988, 1.0433361578501392, 0.9274099180890126, 0.9274099180890126, 1.7388935964168988, 1.391114877133519, 1.0433361578501392, 1.391114877133519, 2.782229754267038, 1.0433361578501392, 1.391114877133519, 1.0433361578501392, 1.391114877133519, 0.9274099180890126, 2.4344510349836583, 1.0433361578501392, 1.0433361578501392, 1.0433361578501392, 1.391114877133519, 1.0433361578501392, 1.1592623976112657, 1.391114877133519, 4.173344631400557, 2.0866723157002784, 2.4344510349836583, 1.0433361578501392, 0.8694467982084494, 2.782229754267038]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfTransformer \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "#compute term counts in text\n",
    "word_counts=[]\n",
    "for term in vocab:\n",
    "    i=terms_list.index(term)\n",
    "    word_counts.append(freq[i])\n",
    "#compute number of documents that contain each term\n",
    "doc_term_counts=[]\n",
    "for i in range(len(dictionary)):\n",
    "    count=0\n",
    "    for j in range(len(docs)):\n",
    "        if (dtm[j][i]!=0):\n",
    "            count=count+1\n",
    "    doc_term_counts.append(count)\n",
    "#compute TFIDF of terms\n",
    "tfidf=[]\n",
    "for i in range(len(dictionary)):\n",
    "    tfidf.append((word_counts[i]*len(docs))/(len(dictionary)*doc_term_counts[i]))\n",
    "print(tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 794,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2id = { dictionary[i] : i for i in range(len(dictionary))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 795,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data_structures': 223, 'cryptography': 727, 'software_engineering': 546, 'computer_graphics': 141, 'network_security': 354, 'computer_programming': 259, 'algorithm_design': 237, 'operating_systems': 158, 'distributed_computing': 258, 'machine_learning': 291}\n"
     ]
    }
   ],
   "source": [
    "#determining term counts of core concepts\n",
    "core_concepts=['data_structures','cryptography','software_engineering','computer_graphics','network_security','computer_programming','algorithm_design','operating_systems','distributed_computing','machine_learning']\n",
    "core_concepts_counts={}\n",
    "for core_concept in core_concepts:\n",
    "    index=word2id[core_concept]\n",
    "    core_concepts_counts[core_concept]=word_counts[index]\n",
    "print(core_concepts_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 796,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data_structures': 0.9017983069789963, 'cryptography': 2.5538901911011824, 'software_engineering': 2.098200892030114, 'computer_graphics': 1.886030746882944, 'network_security': 2.1984583326127933, 'computer_programming': 1.7322055441229876, 'algorithm_design': 1.7725496015088387, 'operating_systems': 0.7906336352053813, 'distributed_computing': 2.5275185795806188, 'machine_learning': 3.212812930522651}\n"
     ]
    }
   ],
   "source": [
    "#determining TF-IDF of core concepts\n",
    "core_concepts_tfidf={}\n",
    "for core_concept in core_concepts:\n",
    "    index=word2id[core_concept]\n",
    "    core_concepts_tfidf[core_concept]=tfidf[index]\n",
    "print(core_concepts_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 797,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_topics=[['data_structures', 'binary_search_tree', 'data_retrieval','graphs'],\n",
    "                   ['cryptography', 'cypher', 'hash','encryption_key'], \n",
    "                   ['software_engineering', 'software_environment', 'software_module', 'malwares'],\n",
    "                   ['computer_graphics', 'graph_partitioning', 'digital_image_processing', 'binary_image'],\n",
    "                   ['network_security', 'network_lifetime', 'channel_attack', 'relay_networks'],\n",
    "                   ['computer_programming','programming_approach','function_calls','debugging'],\n",
    "                   ['algorithm_design','continuous_optimization','inference_algorithm','linearized_model'],\n",
    "                   ['operating_systems','robust_system','virtual_platform','android'],\n",
    "                   ['distributed_computing','web_service','gpu_parallel_computing','cloud_computing_platform'],\n",
    "                   ['machine_learning','statistical_learning','unsupervised_approach','deep_learning_algorithm']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Visualizing Terms Measure**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 798,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            terms  frequency    TF-IDF\n",
      "0                        analysis          3  0.929014\n",
      "1                      artificial         10  0.826604\n",
      "2           artificial_bee_colony          5  1.391115\n",
      "3               color_information          4  0.695557\n",
      "4                cross_validation          4  0.948487\n",
      "...                           ...        ...       ...\n",
      "8256                         makd          6  2.086672\n",
      "8257                        simit          3  2.434451\n",
      "8258              memory_contents          8  1.043336\n",
      "8259  computer_network_technology          3  0.869447\n",
      "8260                  word_clouds         12  2.782230\n",
      "\n",
      "[8261 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_terms=pd.DataFrame(vocab,columns=['terms'])\n",
    "df_terms['frequency']=freq\n",
    "df_terms['TF-IDF']=tfidf\n",
    "print(df_terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 799,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['operating_system', 'multi-proxy_signature', 'uncertainty_measures', 'scoping', 'sanger', 'secondary_studies', 'technical_debt', 'ontology_matching', 'socialbots', 'owl_ontologies', 'geoweb', 'grinding', 'security_game', 'electronic_voting', 'mocas', 'command_queue', 'pretreatment', 'mechatronic_system', 'flame', 'faulty_element', 'lisa', 'memif', 'z-wave']\n"
     ]
    }
   ],
   "source": [
    "filtered_terms=[]\n",
    "for i in range(len(vocab)):\n",
    "    if tfidf[i]>3.3:\n",
    "        filtered_terms.append(vocab[i])\n",
    "print(filtered_terms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtering Terms**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 800,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Determining the most general terms\n",
    "tfidf_max= 3.3 \n",
    "general_words_ids = []\n",
    "for i in range(len(dictionary)):\n",
    "    general_words_ids += [id for id,value in enumerate(tfidf) if (value > tfidf_max)]\n",
    "#Filter Out of the Dictionary\n",
    "#dictionary.filter_tokens(bad_ids=general_words_ids)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 801,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtering low-frequent terms\n",
    "#dictionary.filter_extremes(no_below=2, no_above=1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 802,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8261\n"
     ]
    }
   ],
   "source": [
    "print(len(dictionary))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Computing New Document-Term distribution after Filtering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 803,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('analysis', 'artificial', 'artificial_bee_colony', 'color_information', 'cross_validation', 'explaining', 'handle', 'increase', 'linear_discriminant', 'method', 'neural_networks', 'novel', 'objective', 'principal_component', 'proposed_method', 'recall_rate', 'repetition', 'result', 'state-of-the-art', 'support_vector_machines', 'total', 'vector_machine', 'vision', 'accident', 'clinical_data', 'graphic', 'healthy_subjects', 'movement', 'present_study', 'purpose', 'quantify', 'smoothness', 'subject', 'subjects', 'viewing', 'virtual', 'amounts', 'assists', 'data_entry', 'example', 'export', 'extraction', 'implement', 'input', 'limitation', 'link', 'management_system', 'moving', 'occupancy', 'operating', 'outside', 'package', 'practitioner', 'processing', 'record', 'researcher', 'spatial', 'spatial_distribution', 'tags', 'toolbox', '2016', 'catchment', 'characteristic', 'conservation', 'copyright', 'cross', 'discover', 'entropy', 'geology', 'groundwater', 'guide', 'heterogeneous_environment', 'john_wiley', 'land_use', 'learning_approach', 'ltd.', 'machine', 'maxima', 'modify', 'occurrence', 'percentage', 'prediction_accuracy', 'sample_size', 'sons', 'stream', 'suitability', 'variable', 'advantage', 'analyse', 'common_data_structure', 'data_source', 'facilitates', 'growing_number', 'organisms', 'phylogenetics', 'phylogenies', 'present', 'rich_set', 'uses', 'bitcoin', 'cause', 'create', 'existing_methods', 'fundamental_question', 'future', 'lower_cost', 'mean', 'mechanism', 'mining', 'network_operation', 'operation', 'price', 'quantity', 'rate', 'reduces', 'reward', 'running', 'securing', 'distribution', 'gamma', 'image_processing', 'measure', 'metal', 'probe', 'sheet', 'technique', 'transition', 'consists', 'image_information', 'manufacture', 'practicality', 'product', 'product_design', 'research_field', 'visualization_technology', 'csem', 'divide_and_conquer_approach', 'effective', 'finite_difference', 'finite_element', 'major_advantage', 'major_hurdle', 'outer_region', 'parallel', 'parallel_scalability', 'produce', '3d_reconstruction', '3d_shape', 'diverse_set', 'elsevier_inc._all_rights', 'experimental_result', 'inaccuracy', 'pose', 'prior', 'prior_model', 'propose', 'scene', 'single_image', 'test', 'based', 'clustering_technique', 'dual', 'element', 'engineering', 'fundamental_process', 'fuzzy', 'matching', 'outperforms', 'patch', 'reverse', 'term', 'unsupervised_clustering', 'assess', 'benefit', 'compare', 'computer', 'conduct', 'e.g.', 'geometric_distortion', 'lighting_conditions', 'opinion', 'screen', 'snapshot', 'visual_quality_assessment', 'watermarking', '3d_model', 'advancement', 'based_technique', 'construct', 'data', 'detail', 'disadvantage', 'feasible', 'format', 'image', 'model', 'modern_computer', 'outcome', 'point_cloud', 'printer', 'reconstruction', 'reverse_engineering', 'design_model', 'gesture', 'hand_gesture', 'navigating', 'recognition', 'user', 'user_interaction', 'user_interface', 'ability', 'benchmark', 'extend', 'participant', 'retrieval_algorithm', 'shape_retrieval', 'training_data', 'training_set', 'adaptation', 'consideration', 'dice', 'irregularity', 'potential', 'proposal', 'sample_data', '2015', 'aims', 'battery_life', 'bottleneck', 'delegation', 'demonstrates', 'desirable_properties', 'discrete_logarithm', 'mobile_network', 'mobile_user', 'offer', 'original_signer', 'period', 'proxy_signature', 'proxy_signer', 'signature_scheme', 'signing', 'warrant', 'applicability', 'beta', 'constant_number', 'constructing', 'efficient_algorithm', 'epsilon', 'exhibit', 'fundamental_problem', 'graph_algorithm', 'notion', 'numerous_applications', 'pair', 'publication', 'resource', 'setting', 'shortest_path', 'vast_range', 'vertex', 'exploit', 'lifetime', 'low_power', 'machine-learning', 'many-core', 'meeting', 'performance_gain', 'performance_requirement', 'small-world', 'computational', 'defense', 'multidimensional_data', 'relationship', 'return', 'spatial_data_structure', 'traffic', 'utility', 'abstract_state_machine', 'capture', 'characterizing', 'class', 'defines', 'elsevier_b.v._all_rights', 'essence', 'extension', 'machine_model', 'satisfies', 'sequential', 'update', 'additive_white_gaussian_noise', 'adversary', 'embedding', 'fact', 'filtering', 'peak', 'proposed_technique', 'robust', 'spatial_domain', 'watermark', 'watermarked_image', 'watermarking_scheme', 'worst_case', 'distance_function', 'euclidean', 'furthermore', 'local_structure', 'machine_learning_algorithm', 'manifold_learning', 'neighborhood', 'partial', 'query_point', 'reference', 'respect', 'algorithm', 'assignment', 'clause', 'complexity', 'local', 'polynomial_time', 'search', 'search_algorithm', 'turn', '2017', 'advantages', 'b._v.', 'defect', 'detection_algorithm', 'detection_methods', 'deviation', 'elsevier', 'human_operator', 'online', 'presence', 'radii', 'sample', 'shape', 'shape_model', 'underlying_concept', 'validation', 'vision_system', 'x-ray', 'adding', 'common_way', 'creating', 'cs_education', 'judges', 'learner', 'piece', 'voting', 'bound', 'complement', 'connected_component', 'data_structures', 'et_al.', 'implementing', 'performing', 'recursive', 'cloud_data', 'experiments', 'fast', 'finally', 'fitting', 'generated_model', 'gradient', 'kinds', 'lesion', 'minute', 'requirement', 'visualization_method', 'acceptance', 'assumption', 'black-box', 'complex_model', 'complex_task', 'decision-makers', 'decision_support', 'elsevier_ltd._all_rights', 'explanation', 'flexibility', 'incentive', 'inside', 'learning', 'machine_learning_model', 'novel_use', 'outline', 'possibility', 'prediction_model', 'recommendation', 'sale', 'separation', 'significant_advantage', 'significant_benefit', 'users', 'algorithms', 'attribute', 'capability', 'existing_knowledge', 'foci', 'information', 'knowledge', 'language', 'paper_presents', 'popularization', 'relational', 'series', 'single_type', 'cmos', 'creative_commons_attribution', 'dynamic_range', 'gain', 'imaging', 'raspberry', 'real-world', 'reproduction', 'sensing', 'spectral_response', 'spie', 'the_raspberry_pi', 'account', 'average_time', 'classification_method', 'current', 'disturbance', 'fire_detection', 'kalman', 'nozzle', 'resulting', 'sampling', 'segmenting', 'standard_deviation', 'suppression', 'atmosphere', 'cover', 'impact', 'minimizes', 'simple', 'time_consuming', 'algorithm_design', 'configuration', 'monitoring_system', 'proposed_model', 'risk', 'risk_management', 'scoring', 'system_design', 'correspondence', 'define', 'hamming', 'iterative_approach', 'optimal_solution', 'option', 'relation', 'representative', 'restriction', 'aspect', 'averaging', 'classification_model', 'cross-validation', 'dimensionality_reduction', 'elsevier_b.v.', 'feature_subset', 'predictive_performance', 'split', 'test_set', 'unsupervised_learning', 'data_fusion', 'duplicate', 'experimental', 'order_relation', 'priori', 'tuple', 'development_methodology', 'development_process', 'maturity', 'multi-agent', 'organization', 'software', 'tuning', 'usefulness', 'accurate_prediction', 'based_algorithm', 'computer_vision', 'damage_detection', 'identifying', 'improvement', 'integrating', 'laser', 'measurement', 'noise_level', 'non-contact', 'optical_flow', 'relative', 'skew', 'stiffness', 'structural', 'structural_damage', 'structural_properties', 'system_identification', 'video', 'attacker', 'attempt', 'evaluation_result', 'improved_method', 'incoming_packet', 'intend', 'internet', 'internet_users', 'malicious_activities', 'malicious_packets', 'multiple_attributes', 'sender', 'spikes', 'tactics', 'computer_program', 'database_management', 'examples', 'multiple_tables', 'records', 'design_approach', 'execution', 'fluent', 'fluid_dynamics', 'instance', 'multi-objective_optimization_problem', 'non-dominated_sorting_genetic_algorithm', 'nsga-ii', 'antenna', 'lead', 'main_reason', 'operating_systems', 'technical_capabilities', 'technical_characteristics', 'boundary_condition', 'core', 'discrete', 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'filterbank', 'capsule', 'laplace-beltrami', 'ray-tracing', 'token', 'hour', 'heterogeneous_distributed_computing_environment', 'medical_image_registration', 'practical_problem', 'highway', 'intelligent_vehicle', 'pattern_classification', 'multiple_regression_model', 'vector_data', 'web_interface', 'discussion_forum', 'social_bot', 'excellent_platform', 'malicious_accounts', \"people's_life\", 'obda', 'ontology-based_data_access', 'command_queue', 'negligible_runtime', 'runtime_system', 'complex_experiments', 'bioavailability', 'benchmark_function', 'knowledge_gap', 'gas_flow', 'pore_space', 'orientation', 'nurse', 'troubleshooting', 'school', 'precision', 'red-black_tree', 'doubt', 'ecdsa', 'tenant', 'regularizer', 'human_detection', 'increased_attention', 'unique_opportunity', 'path_planning', 'ubiquity', 'multi-source_data', 'ceiling', 'cpu_cores', 'directed_acyclic_graph', 'worst-case', 'ears', 'strip', 'signature_verification', 'typing', 'composition', 'consciousness', 'classroom_teaching', 'confidence_level', 'peer', 'programming_and_programming_languages', 'system_error', 'wavefront', 'needle', 'simplex', 'special_focus', 'asphalt', 'fringe', 'galaxy', 'redshifts', 'contour_lines', 'light_detection', 'pevs', 'hamiltonian', 'system_size', 'online_tool', 'physical_memory', 'time_window', 'symbian', 'portfolio', 'pivotal_role', 'wind_power', 'protein_structure', 'lower_frequency', 'geometrical_characteristics', 'spacing', 'provenance', 'technical_disciplines', 'computer-aided_software_engineering', 'functional_requirements', 'probabilistic_inference', 'additional_time', 'certain_class', 'vegetables', 'interactive_projection', 'speedup_factor', 'cavities', 'topological_properties', 'solvent', 'certain_amount', 'multiple_threads', 'secondary_structure', 'tumour', 'human_computer_interaction', 'information_integration', 'bindings', 'certificate_revocation_lists', 'equivalent_key', 'key_recovery_attack', 'cfrp', 'laplacian_eigenmap', 'design/methodology/approach', 'gbed', 'head-mounted_display', 'originality/value', 'research_limitations/implications', 'social_implications', 'visual_reality', 'codification', 'readiness', 'practical_value', 'qos_constraints', 'antarctica', 'electronics', 'purpose_the', 'teleoperation', 'curriculum', 'catalog', 'query_answering', 'semantic_relationship', 'stackoverflow', 'smartphone_security', 'diagnostic_accuracy', 'pretreatment', 'dementia', 'city-scale', 'fisher', 'manufacturer', 'geometric_data_structures', 'individual_system', 'privacy_amplification', 'photon_number_splitting', 'qubits', 'multitask', 'stacking', 'cryptographic_module', 'inference_algorithm', 'data_repositories', 'turbidity', 'shrinkage', 'additional_comments', 'chemical_reactions', 'hydrogen', 'object-oriented_program', 'stitching', 'construction_site', 'failing', 'occlusion', 'sensing_technologies', 'simulation_technology', 'level', 'scientific_visualization', 'lasso', 'fundamental_mechanism', 'huge_computation', 'instructional_intervention', 'smartphone_platform', 'catch', 'representative_set', 'software_engineering_domain', 'query_relaxation', 'theoretical_properties', 'triple_patterns', 'verifiable_secret_sharing', 'parameter_study_job', 'discriminant_feature', 'sqlite', 'client_side', 'integer_factorization_problem', 'lookup_table', 'multi-threading', 'desired_feature', 'threshold_scheme', 'visual_secret_sharing', 'huge_number', \"wei_et_al.'s\", 'cloud_server', 'street', 'mating', 'heterogeneous_parts', 'rectangular_dissections', 'integral_representation', 'illusion', 'industrial_project', 'refactoring_sequence', 'art_algorithm', 'role-playing_game', 'opensees', 'mathematical_programming', 'regression_testing', 'moea/d', 'relevant_literature', 'database_content', 'scalable_application', 'alloy', 'architectural_style', 'scalable_system', 'soap', 'spline', 'nanotechnology', 'posing', 'software_repository', 'scripting_language', 'virtual_platform', 'histogram_modification', 'reversibility', 'ancilla', 'reversible_logic', 'public-key_encryption', 'iran', 'orthopedic_surgeons', 'assembles', 'chinese_characters', 'prey', 'bogie', 'moving_train', 'rolling_stock_examination', 'real-time_os', 'single_table', 'essential_role', 'simplified_version', 'safety', 'superpixel_level', 'salmon', 'companies', 'money', 'text_detection', 'cumulants', 'resource_provider', 'configures', 'restart', 'constructive_method', 'digital_fabrication', 'f-measure', 'study_group', 'weight_value', 'future_evolution', 'sdn_architecture', 'interactive_simulation', 'cryptography_algorithms', 'iot_devices', 'symmetric_key_cryptography', 'affinity', 'business_services', 'critical_analysis', 'existing_approach', 'garment', 'text-mining', 'valence', 'thread-level_parallelism', 'farash', 'applications_tool', 'avispa', 'formal_security_analysis', 'communication_system_security', 'disclosure', 'underlying_idea', '3d_technologies', 'fibre', 'structured_documents', 'molecular_dynamics_simulations', 'tcam', 'server_side', 'plaque', 'potential_threats', 'stata', 'avionics', 'interaction_device', 'analogue', 'ntru', 'user_input', 'current_effort', 'unified_model', 'phase_ii', 'zigbee', 'smart_mobile_device', 'core_idea', 'smartphones', 'research_team', 'customer_satisfaction', 'cybernetics', 'software_cybernetics', 'harmony', 'sdns', 'km_practices', 'computer-aided_software_engineering_tool', 'main_area', 'proper_choice', 'stochastic_nature', 'proof', 'mathematical_modeling', 'software_engineering_measures', 'tcp/ip', 'interactive_model', 'cache_replacement_policy', 'mechatronic_system', 'increased_number', 'sandy_bridge', 'predictive_power', 'wide_class', 'grass_gis', 'scientific_field', 'affinity_matrix', 'quaternion', 'substation_automation', 'visual_vocabulary', 'weka', 'water_quality', 'phase_field', 'cyberinfrastructure', 'hindi', 'artefact', 'computer_skills', 'semi-supervised_classification', 'vein', 'active_system', 'subgroup', 'burrows-wheeler', 'suffix_array_construction_algorithm', 'theta', 'correct_classification_rate', 'cost_optimization', 'r-tree', 'sublinear', 'maximum_diameter', 'surface_mesh', 'field_surveys', 'dramatic_growth', 'coloring_algorithm', 'kuhn', 'brain_project', 'global_memory', 'task_scheduling_algorithm', 'k-12_classrooms', 'distributed_classification', 'efficient_computation', 'tmis', 'terrestrial_laser_scanning', 'description_method', 'grid_cells', 'canada', 'meteorological_data', 'portable_device', 'large-eddy_simulation', 'traffic_engineering', 'electromagnetic_wave', 'iccg_method', 'labview', 'remote_access', 'mahalanobis_distance', 'lego_mindstorms', 'openacc', 'history', 'user-friendly_graphical_interface', 'transmitted_data', 'interrelation', 'interconnection', 'distributed_computing_networks', 'irregular_objects', 'algebraic_operations', 'upper_and_lower_bounds', 'atlas_experiment', 'catalogue', 'eventindex', 'forward_operator', 'multi-core_architecture', 'biologists', 'computer_vision_community', 'multiuser_interference', 'deep_analysis', 'belle_ii_experiment', 'annihilation', 'institute', 'common_divisor', 'vocational_college', 'clearance', 'museums', 'average', 'explosive_hazard_detection', 'explosive_hazards', 'distribution_parameter', 'plastic', 'nonlinear_dynamical_systems', 'circle', 'complete_binary_tree', 'mobius', 'faulty_set', 'object_properties', 'image_manipulation', 'ancestor', 'np-hard_problems', 'planar_graphs', 'system_behavior', 'core_aspect', 'qt_framework', 'kinetic_energy', 'debate', 'distributed_representation', 'distributed_optimization', 'food_safety', 'categorical_data', 'system_operator', 'adaptive_software', 'kids', 'accommodation', 'border', 'influenza', 'kinect_sensor', 'proposed_device', 'proposed_pipeline', 'multi-server_architecture', 'demand_response', 'pump', 'emerging_use', 'current_technology', 'shared_memory_system', 'failure_detector', 'posture', 'switzerland', 'heliostat', 'heliostat_field', 'heliostats', 'fptas', 'operation_time', 'smart_cities', 'triangulated_surfaces', 'null_value', 'clothing', 'traditional_implementation', 'ads-b', 'specialty', 'binary_decision_diagram', 'code.org', 'flame', 'ignition', 'linked_open_data', 'rational_unified_process', 'reallocation', 'cent', 'transponder', 'nearest_neighbor_algorithm', 'searchable_encryption', 'research_group', 'real_world_data', 'computer_data', 'safe_operation', 'hu_moments', 'genus', 'tremendous_growth', 'connected_subgraph', 'faulty_element', 'k-ary_n-cube', 'structure_fault', 'distributed_manner', 'ls-svm', 'convergence_analysis', 'legendre', 'problem_data', 'lbp_codes', 'cell_mapping_methods', 'characterization', 'coatings', 'glcm', 'substantial_improvements', 'textile', 'transient_faults', 'message', 'fluorescence', 'arp_spoofing_attack', 'trajectory', 'e._coli', 'potential_benefit', 'increasing_trend', 'mapping_process', 'processing_nodes', 'idps', 'unified_modeling_language', 'learning_levels', 'solar', 'poset', 'information_modeling', 'soil-structure_interaction', 'vehicle_tracking', 'forging', 'subject_domain', 'electric_energy', 'powertrain_system', 'bankruptcy', 'adjacency', 'brevity', 'digital_topology', 'lung_cancer', 'marker', 'computational_effort', 'gray_level', 'biomass', 'volatile_memory', 'comput_appl_eng_educ_24', 'distraction', 'surgery', 'supporting_point', 'tunnelling', 'parallel_mode', 'real-time_simulation', 'quick_response', 'linearizability', 'code_reusability', 'visual_content', 'primary_goal', 'particular_attention', 'equilibrium_computation', 'ban_logic', 'pole', 'android_os', 'checker', 'vacuum', 'departure', 'critical_point', 'eruption', 'final_state', 'real_darknet_traffic', 'investigations', 'archived_data', 'benchmark_programs', 'program_transformation', 'halves', 'basic_characteristics', 'non-destructive_testing', 'trip', 'machine_vision_system', 'scanning_scheme', 'recipe', 'mobile_data_collection_applications', 'mobile_service', 'technical_issues', 'online_survey', 'kenya', 'mobile_usability', 'nucleation', 'comput_appl_eng_educ_23', 'emulation', 'lisa', 'asynchronous_message-passing_system', 'galerkin', 'core_element', 'cellular_networks', 'distributed_algorithm_design', 'step', 'power_control', 'inventory_control', 'expert_judgment', 'tiling', 'portfolio_solver', 'dns_service', 'connector', 'building_information_models', 'laboratory_exercises', 'gnu_general_public_license', 'e-commerce', 'embedded_security', 'postgraduate_students', 'self-efficacy', 'wearable_technology', 'security_investments', 'aec_curricula', 'unlabeled_samples', 'coordination', 'local_search_procedure', 'cbse', 'set_s', 'space-filling_curve', 'commonalities', 'discrete_system', 'opendss', 'content-based_image_retrieval', 'r2rml', 'channel_attack', 'solar_orbiter', 'atlas', 'viable_alternative', 'blank', 'trace_table', 'rule-based_approach', 'stripe', 'bit_key_length', 'derived_correlation', 'dialog', 'gate_equivalents', 'oracle_inequality', 'thermograms', 'sequence', 'tsunami_wave', 'vane', 'lotka-volterra', 'self-calibration', 'transit', 'terahertz', 'guis', 'aforementioned_problem', 'quantum_secret_sharing', 'antiport_rules', 'symport/antiport_rule', 'symport_rules', 'tissue_p_systems', 'memif', 'software_management', 'ders', 'shopping', 'fundamental_importance', 'illness', 'pc_cluster', 'warning_system', 'surgical_training', 'isosurfaces', 'stochastic_programming', 'corona', 'singular_integral', 'approximation_factor', 'cs_plugged', 'cs_unplugged', 'teleportation', 'kd-trees', 'volume_hierarchies', 'printing_technology', 'polarization', 'entanglement_concentration', 'waveguides', 'cmes', 'albedo', 'hw/sw', 'pelc', 'xpath', 'iaas', 'z-wave', 'makd', 'simit', 'memory_contents', 'computer_network_technology', 'word_clouds')\n"
     ]
    }
   ],
   "source": [
    "vocab=tuple(dictionary[i] for i in range(len(dictionary)))\n",
    "print(vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 804,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_docs=[]\n",
    "for doc in docs:\n",
    "    new_doc=[]\n",
    "    for word in doc:\n",
    "        if(word in vocab):\n",
    "            new_doc.append(word)\n",
    "    new_docs.append(new_doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 805,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dtm=[]\n",
    "for i in range(len(new_docs)):\n",
    "  doc_counts=[]\n",
    "  for j in range(len(dictionary)):\n",
    "    if (dictionary[j] in new_docs[i]):\n",
    "      doc_counts.append(1)\n",
    "    else:\n",
    "      doc_counts.append(0)\n",
    "  new_dtm.append(doc_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 806,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5746, 8261)\n",
      "102103\n"
     ]
    }
   ],
   "source": [
    "new_dtm=np.array(new_dtm)\n",
    "print(new_dtm.shape)\n",
    "print(new_dtm.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating Seeded LDA Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 807,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_topic_list = [['data_structures', 'binary_search_tree', 'data_retrieval','graphs'],\n",
    "                   ['cryptography', 'cypher', 'hash','encryption_key'], \n",
    "                   ['software_engineering', 'software_environment', 'software_module', 'malwares'],\n",
    "                   ['computer_graphics', 'graph_partitioning', 'digital_image_processing', 'binary_image'],\n",
    "                   ['network_security', 'network_lifetime', 'channel_attack', 'relay_networks'],\n",
    "                   ['computer_programming','programming_approach','function_calls','debugging'],\n",
    "                   ['algorithm_design','continuous_optimization','inference_algorithm','linearized_model'],\n",
    "                   ['operating_systems','robust_system','virtual_platform','android'],\n",
    "                   ['distributed_computing','web_service','gpu_parallel_computing','cloud_computing_platform'],\n",
    "                   ['machine_learning','statistical_learning','unsupervised_approach','deep_learning_algorithm']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 808,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = glda.GuidedLDA(n_topics=10, n_iter=100, random_state=7, refresh=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 809,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2id = { dictionary[i] : i for i in range(len(dictionary))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 810,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "seed_topics = {}\n",
    "for t_id, st in enumerate(seed_topic_list):\n",
    "     for word in st:\n",
    "         seed_topics[word2id[word]] = t_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 811,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{353: 0, 6235: 0, 1056: 0, 3897: 0, 639: 1, 7526: 1, 3511: 1, 3808: 1, 1282: 2, 6109: 2, 4550: 2, 6231: 2, 2440: 3, 7277: 3, 1030: 3, 4692: 3, 898: 4, 1615: 4, 8198: 4, 6994: 4, 1966: 5, 6025: 5, 7236: 5, 1340: 5, 438: 6, 3107: 6, 7758: 6, 1159: 6, 530: 7, 3564: 7, 7825: 7, 1888: 7, 2697: 8, 4314: 8, 6352: 8, 6451: 8, 605: 9, 5808: 9, 4389: 9, 5171: 9}\n"
     ]
    }
   ],
   "source": [
    "print(seed_topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 812,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lda:n_documents: 5746\n",
      "INFO:lda:vocab_size: 8261\n",
      "INFO:lda:n_words: 102103\n",
      "INFO:lda:n_topics: 10\n",
      "INFO:lda:n_iter: 100\n",
      "INFO:lda:<0> log likelihood: -1346729\n",
      "INFO:lda:<20> log likelihood: -921178\n",
      "INFO:lda:<40> log likelihood: -901042\n",
      "INFO:lda:<60> log likelihood: -889592\n",
      "INFO:lda:<80> log likelihood: -881462\n",
      "INFO:lda:<99> log likelihood: -875577\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<lda.guidedlda.GuidedLDA at 0x1f6e02b1460>"
      ]
     },
     "execution_count": 812,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(new_dtm, seed_topics=seed_topics, seed_confidence=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Topic-Word Distributions (Top 20 Words)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 813,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8261\n"
     ]
    }
   ],
   "source": [
    "print(len(vocab))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 814,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8261\n"
     ]
    }
   ],
   "source": [
    "print(len(dictionary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 815,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 0: result present analysis 2016 data_structures operating propose relational technique processing implement operation parallel data 2015 paper_presents running elsevier_b.v._all_rights query method\n",
      "Topic 1: cryptography propose result present encryption secure 2016 term analysis quanta method uses proposed_scheme complexity computational furthermore sons et_al. john_wiley ltd.\n",
      "Topic 2: engineering result software_engineering present method 2016 analysis software help learning challenge elsevier_b.v._all_rights paper_presents requirement researcher technique developer benefit future form\n",
      "Topic 3: propose present ltd. john_wiley sons copyright analysis finally internet result relational network_security method foci technique 2016 uses threat attacker detecting\n",
      "Topic 4: result method machine 2016 learning analysis elsevier_ltd._all_rights elsevier_b.v._all_rights processing 2017 present proposed_method vision characteristic image propose mean resulting extraction imaging\n",
      "Topic 5: learning result present computer_programming method computer analysis engineering computational help relational skill machine create subject teach thinking university content foci\n",
      "Topic 6: method parallel result present propose 2016 vision state-of-the-art experimental_result processing computational computer graphic finally proposed_method solving elsevier_b.v._all_rights term advantage analysis\n",
      "Topic 7: operating analysis propose present operating_systems result internet android relational e.g. consists researcher things foci making paper_presents implement linux example help\n",
      "Topic 8: result propose parallel present resource term processing experimental_result 2016 execution implement increase scheduling analysis challenge operating distributed distributed_computing method requirement\n",
      "Topic 9: method result processing learning machine propose 2017 elsevier_b.v._all_rights analysis proposed_method experimental_result 2016 present vision state-of-the-art technique finally elsevier_ltd._all_rights image_processing graphic\n"
     ]
    }
   ],
   "source": [
    "n_top_words = 20\n",
    "topic_word = model.topic_word_\n",
    "for i, topic_dist in enumerate(topic_word):\n",
    "     topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n_top_words+1):-1]\n",
    "     print('Topic {}: {}'.format(i, ' '.join(topic_words)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Saving Seeded LDA Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 816,
   "metadata": {},
   "outputs": [],
   "source": [
    "from six.moves import cPickle as pickle\n",
    "with open('guidedlda_model.pickle', 'wb') as file_handle:\n",
    "     pickle.dump(model, file_handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 817,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1346728.7302988248, -921178.2147164315, -901041.9159765346, -889591.5758010605, -881461.6313273335]\n"
     ]
    }
   ],
   "source": [
    "beta=model.topic_word_\n",
    "theta=model.transform(new_dtm)\n",
    "logs=model.loglikelihoods_\n",
    "print(logs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 818,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5746, 10)\n"
     ]
    }
   ],
   "source": [
    "theta=np.asarray(theta)\n",
    "print(theta.shape)\n",
    "docs_length=[]\n",
    "for doc in docs:\n",
    "  docs_length.append(len(doc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 819,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "normalized_theta=preprocessing.normalize(theta, norm='l2')\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Computing Term Frequency of Core Concepts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 820,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10853.402866     838.4715473     62.49317342 ...    35.18192998\n",
      "    51.82280905    17.79839592]\n"
     ]
    }
   ],
   "source": [
    "#Compute term_frequency\n",
    "topic_freq= (theta.T * docs_length).T.sum()\n",
    "term_topic_freq = (beta.T * topic_freq).T\n",
    "term_frequency = np.sum(term_topic_freq, axis=0)\n",
    "print(term_frequency)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 821,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data_structures': 2170.9594421185097, 'cryptography': 2874.2643815093174, 'software_engineering': 2064.267046896593, 'computer_graphics': 621.1158912365482, 'network_security': 1713.0756229318965, 'computer_programming': 1520.8544001406046, 'algorithm_design': 1062.6952794151662, 'operating_systems': 1989.332904890316, 'distributed_computing': 677.8755327137357, 'machine_learning': 674.0698636156336}\n"
     ]
    }
   ],
   "source": [
    "core_concepts=['data_structures','cryptography','software_engineering','computer_graphics','network_security','computer_programming','algorithm_design','operating_systems','distributed_computing','machine_learning']\n",
    "core_concepts_freq={}\n",
    "for core_concept in core_concepts:\n",
    "    index=word2id[core_concept]\n",
    "    core_concepts_freq[core_concept]=term_frequency[index]\n",
    "print(core_concepts_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Computing Saliency of Core Concepts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 822,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the distinctiveness and saliency of the terms:\n",
    "# this determines the R terms that are displayed when no topic is selected\n",
    "term_proportion = term_frequency / term_frequency.sum()\n",
    "topic_proportion = (topic_freq / topic_freq.sum())\n",
    "topic_given_term = beta /beta.sum()\n",
    "kernel = (topic_given_term* np.log((topic_given_term.T / topic_proportion).T))\n",
    "distinctiveness = kernel.sum()\n",
    "saliency = term_proportion * distinctiveness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 823,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute term counts in text\n",
    "word_counts=[]\n",
    "for term in vocab:\n",
    "    i=terms_list.index(term)\n",
    "    word_counts.append(freq[i])\n",
    "#compute number of documents that contain each term\n",
    "doc_term_counts=[]\n",
    "for i in range(len(dictionary)):\n",
    "    count=0\n",
    "    for j in range(len(docs)):\n",
    "        if (dtm[j][i]!=0):\n",
    "            count=count+1\n",
    "    doc_term_counts.append(count)\n",
    "#computing tf-idf after filtering\n",
    "tfidf=[]\n",
    "for i in range(len(dictionary)):\n",
    "    tfidf.append((word_counts[i]*len(docs))/(len(dictionary)*doc_term_counts[i])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 824,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_docs=[]\n",
    "for doc in docs:\n",
    "    new_doc=[]\n",
    "    for word in doc:\n",
    "        if(word in vocab):\n",
    "            new_doc.append(word)\n",
    "    new_docs.append(new_doc)\n",
    "new_dtm=[]\n",
    "for i in range(len(new_docs)):\n",
    "  doc_counts=[]\n",
    "  for j in range(len(dictionary)):\n",
    "    if (dictionary[j] in new_docs[i]):\n",
    "      doc_counts.append(1)\n",
    "    else:\n",
    "      doc_counts.append(0)\n",
    "  new_dtm.append(doc_counts)\n",
    "new_dtm=np.array(new_dtm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 825,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Terms    TF-IDF  term counts  Saliency\n",
      "0                        analysis  0.929014         1158 -0.078970\n",
      "1                      artificial  0.826604           82 -0.006101\n",
      "2           artificial_bee_colony  1.391115           12 -0.000455\n",
      "3               color_information  0.695557            4 -0.000319\n",
      "4                cross_validation  0.948487           15 -0.000808\n",
      "...                           ...       ...          ...       ...\n",
      "8256                         makd  2.086672            6 -0.000219\n",
      "8257                        simit  2.434451            7 -0.000160\n",
      "8258              memory_contents  1.043336            3 -0.000256\n",
      "8259  computer_network_technology  0.869447            5 -0.000377\n",
      "8260                  word_clouds  2.782230            8 -0.000130\n",
      "\n",
      "[8261 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "#determining measures of all terms\n",
    "terms_saliency={}\n",
    "for term in vocab:\n",
    "    index=word2id[term]\n",
    "    terms_saliency[term]=saliency[index]\n",
    "df_terms=pd.DataFrame(vocab,columns=['Terms'])\n",
    "df_terms['TF-IDF']=tfidf\n",
    "df_terms['term counts']=word_counts\n",
    "df_terms['Saliency']=terms_saliency.values()\n",
    "print(df_terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 826,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data_structures': -0.01579600920086729, 'cryptography': -0.02091329102479285, 'software_engineering': -0.015019710010798883, 'computer_graphics': -0.004519270209490089, 'network_security': -0.012464423690572358, 'computer_programming': -0.01106581248443659, 'algorithm_design': -0.007732223866411531, 'operating_systems': -0.014474485455412816, 'distributed_computing': -0.004932256192377299, 'machine_learning': -0.004904565954170723}\n"
     ]
    }
   ],
   "source": [
    "core_concepts_saliency={}\n",
    "for core_concept in core_concepts:\n",
    "    index=word2id[core_concept]\n",
    "    core_concepts_saliency[core_concept]=saliency[index]\n",
    "print(core_concepts_saliency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Computing Relevance of Core Concepts**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 827,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2id = { dictionary[i] : i for i in range(len(dictionary))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 828,
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting distribution matrices into dataframes\n",
    "def _df_with_names(data, index_name, columns_name):\n",
    "   if type(data) == pd.DataFrame:\n",
    "      # we want our index to be numbered\n",
    "      df = pd.DataFrame(data.values)\n",
    "   else:\n",
    "      df = pd.DataFrame(data)\n",
    "   df.index.name = index_name\n",
    "   df.columns.name = columns_name\n",
    "   return df\n",
    "## compute relevance each topic\n",
    "topic_term_dists = _df_with_names(beta, 'topic', 'term')\n",
    "doc_topic_dists  = _df_with_names(beta, 'doc', 'topic')\n",
    "log_lift = np.log(topic_term_dists / term_proportion)\n",
    "log_ttd = np.log(topic_term_dists)\n",
    "R=len(vocab)\n",
    "def _find_relevance(log_ttd, log_lift, R, lambda_):\n",
    "   relevance_measure= lambda_ * log_ttd + (1 - lambda_) * log_lift\n",
    "   return relevance_measure.T\n",
    "relevance_0=_find_relevance(log_ttd, log_lift, R, lambda_=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 829,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevance_1=_find_relevance(log_ttd, log_lift, R, lambda_=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 830,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevance_2=_find_relevance(log_ttd, log_lift, R, lambda_=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 831,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "topic         0         1         2         3         4         5         6  \\\n",
      "term                                                                          \n",
      "0      0.166995 -0.088798  0.003307  0.012776  0.275977  0.012326 -0.629079   \n",
      "1     -6.388491 -6.367567 -6.695265 -6.159037  0.621798  1.716693 -6.734212   \n",
      "2      1.511337 -3.771043 -4.098742 -3.562513 -3.872588 -3.726264 -4.137688   \n",
      "3     -3.436570 -3.415646 -3.743344 -3.207115  1.786115 -3.370866 -3.782290   \n",
      "4     -4.367125 -4.346201 -4.673899 -4.137671  1.546216 -4.301422 -4.712846   \n",
      "...         ...       ...       ...       ...       ...       ...       ...   \n",
      "8256  -3.060485  2.263744 -3.367259 -2.831031 -3.141106 -2.994781 -3.406206   \n",
      "8257  -2.749278 -2.728354  2.247253 -2.519824 -2.829899 -2.683575 -3.094999   \n",
      "8258  -3.217443 -3.196519 -3.524217 -2.987989 -3.298063 -3.151739 -3.563163   \n",
      "8259   2.102369 -3.583816 -3.911515 -3.375286 -3.685361 -3.539037 -3.950461   \n",
      "8260  -2.536019 -2.515094 -2.842793 -2.306564 -2.616639 -2.470315 -2.881739   \n",
      "\n",
      "topic         7         8         9  \n",
      "term                                 \n",
      "0      0.294859 -0.472268  0.055648  \n",
      "1     -6.204122 -6.820985  0.940237  \n",
      "2     -3.607598 -4.224462  1.667070  \n",
      "3     -3.252201 -3.869064  1.331811  \n",
      "4     -4.182756 -4.799620  1.650459  \n",
      "...         ...       ...       ...  \n",
      "8256  -2.876116 -3.492979 -3.595409  \n",
      "8257  -2.564909 -3.181773 -3.284202  \n",
      "8258   2.270231 -3.649937 -3.752367  \n",
      "8259  -3.420372 -4.037235  0.475456  \n",
      "8260  -2.351649 -2.968513  2.232363  \n",
      "\n",
      "[8261 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "print(relevance_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 832,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  topics\\core concepts  machine_learning  distributed_computing  \\\n",
      "0              topic 0         -6.170244              -6.175874   \n",
      "1              topic 1         -6.149320              -0.447839   \n",
      "2              topic 2         -6.477018              -6.482648   \n",
      "3              topic 3         -5.940790              -5.946420   \n",
      "4              topic 4          1.063022              -0.549384   \n",
      "5              topic 5          0.986370              -6.110170   \n",
      "6              topic 6         -6.515965              -6.521595   \n",
      "7              topic 7         -5.985875              -5.991505   \n",
      "8              topic 8         -6.602738               2.171343   \n",
      "9              topic 9          1.483799              -6.710798   \n",
      "\n",
      "   operating_systems  algorithm_design  computer_programming  \\\n",
      "0           0.444202          0.378500             -6.983938   \n",
      "1          -7.231541         -0.897439             -6.963014   \n",
      "2          -1.160644         -6.932248             -0.604851   \n",
      "3          -7.023011          0.289841             -6.754484   \n",
      "4          -7.333085         -6.706094             -1.070597   \n",
      "5          -7.186761          0.126091              2.208833   \n",
      "6          -7.598185          1.035506             -7.329658   \n",
      "7           1.881010         -6.441105             -6.799569   \n",
      "8           0.446866          1.044012             -7.416432   \n",
      "9          -7.787389         -7.160398             -7.518862   \n",
      "\n",
      "   network_security  computer_graphics  software_engineering  cryptography  \\\n",
      "0         -7.102956           1.082460             -7.289441     -7.620462   \n",
      "1         -0.173277           0.485004             -7.268517      2.252709   \n",
      "2         -7.409730           0.983182              2.032902     -7.927236   \n",
      "3          1.720838           0.134988             -0.056012     -1.397047   \n",
      "4         -7.183576          -6.169048             -7.370061     -7.701083   \n",
      "5         -7.037252          -6.022724             -0.671229     -7.554759   \n",
      "6         -7.448677          -6.434149             -7.635161     -7.966183   \n",
      "7          0.778080          -5.904059             -0.101098     -1.442132   \n",
      "8          0.328201          -6.520922             -7.721935     -8.052957   \n",
      "9         -7.637880           0.467558             -7.824365     -8.155386   \n",
      "\n",
      "   data_structures  \n",
      "0         1.659908  \n",
      "1        -7.318911  \n",
      "2        -0.737854  \n",
      "3        -7.110380  \n",
      "4         0.075642  \n",
      "5        -0.365376  \n",
      "6         0.287255  \n",
      "7        -1.161504  \n",
      "8        -0.170927  \n",
      "9        -7.874759  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "<>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "<ipython-input-832-13c7942228a7>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "  df_relevance_0=pd.DataFrame(topics,columns=['topics\\core concepts'])\n"
     ]
    }
   ],
   "source": [
    "# Determining relevance of core concepts\n",
    "topics=['topic 0','topic 1','topic 2','topic 3','topic 4','topic 5','topic 6','topic 7','topic 8','topic 9']\n",
    "df_relevance_0=pd.DataFrame(topics,columns=['topics\\core concepts'])\n",
    "for core_concept in core_concepts:\n",
    "    index=word2id[core_concept]\n",
    "    df_relevance_0.insert(1,core_concept,relevance_0.loc[index])\n",
    "print(df_relevance_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 833,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "<>:3: DeprecationWarning: invalid escape sequence \\c\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  topics\\core concepts  machine_learning  distributed_computing  \\\n",
      "0              topic 0         -9.931093              -9.933908   \n",
      "1              topic 1         -9.910169              -4.205873   \n",
      "2              topic 2        -10.237867             -10.240682   \n",
      "3              topic 3         -9.701639              -9.704454   \n",
      "4              topic 4         -2.697827              -4.307418   \n",
      "5              topic 5         -2.774479              -9.868204   \n",
      "6              topic 6        -10.276814             -10.279628   \n",
      "7              topic 7         -9.746724              -9.749539   \n",
      "8              topic 8        -10.363587              -1.586691   \n",
      "9              topic 9         -2.277050             -10.468832   \n",
      "\n",
      "   operating_systems  algorithm_design  computer_programming  \\\n",
      "0          -2.775536         -3.154734            -10.337940   \n",
      "1         -10.451279         -4.430673            -10.317016   \n",
      "2          -4.380382        -10.465482             -3.958853   \n",
      "3         -10.242749         -3.243393            -10.108486   \n",
      "4         -10.552824        -10.239328             -4.424599   \n",
      "5         -10.406500         -3.407143             -1.145169   \n",
      "6         -10.817924         -2.497728            -10.683660   \n",
      "7          -1.338729         -9.974339            -10.153571   \n",
      "8          -2.772873         -2.489221            -10.770434   \n",
      "9         -11.007127        -10.693632            -10.872864   \n",
      "\n",
      "   network_security  computer_graphics  software_engineering  cryptography  \\\n",
      "0        -10.397449          -2.719297            -10.490691    -10.656202   \n",
      "1         -3.467770          -3.316753            -10.469767     -0.783031   \n",
      "2        -10.704223          -2.818575             -1.168349    -10.962976   \n",
      "3         -1.573655          -3.666769             -3.257263     -4.432786   \n",
      "4        -10.478069          -9.970805            -10.571312    -10.736823   \n",
      "5        -10.331745          -9.824481             -3.872480    -10.590498   \n",
      "6        -10.743169         -10.235905            -10.836412    -11.001923   \n",
      "7         -2.516413          -9.705816             -3.302348     -4.477872   \n",
      "8         -2.966292         -10.322679            -10.923186    -11.088696   \n",
      "9        -10.932373          -3.334199            -11.025615    -11.191126   \n",
      "\n",
      "   data_structures  \n",
      "0        -1.516146  \n",
      "1       -10.494964  \n",
      "2        -3.913908  \n",
      "3       -10.286434  \n",
      "4        -3.100411  \n",
      "5        -3.541430  \n",
      "6        -2.888798  \n",
      "7        -4.337558  \n",
      "8        -3.346980  \n",
      "9       -11.050812  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-833-a46f3be7eb2a>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "  df_relevance_1=pd.DataFrame(topics,columns=['topics\\core concepts'])\n"
     ]
    }
   ],
   "source": [
    "# Determining relevance of core concepts\n",
    "topics=['topic 0','topic 1','topic 2','topic 3','topic 4','topic 5','topic 6','topic 7','topic 8','topic 9']\n",
    "df_relevance_1=pd.DataFrame(topics,columns=['topics\\core concepts'])\n",
    "for core_concept in core_concepts:\n",
    "    index=word2id[core_concept]\n",
    "    df_relevance_1.insert(1,core_concept,relevance_1.loc[index])\n",
    "print(df_relevance_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 834,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "<>:3: DeprecationWarning: invalid escape sequence \\c\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  topics\\core concepts  machine_learning  distributed_computing  \\\n",
      "0              topic 0        -13.691942             -13.691942   \n",
      "1              topic 1        -13.671018              -7.963907   \n",
      "2              topic 2        -13.998716             -13.998716   \n",
      "3              topic 3        -13.462488             -13.462488   \n",
      "4              topic 4         -6.458676              -8.065452   \n",
      "5              topic 5         -6.535328             -13.626238   \n",
      "6              topic 6        -14.037662             -14.037662   \n",
      "7              topic 7        -13.507573             -13.507573   \n",
      "8              topic 8        -14.124436              -5.344725   \n",
      "9              topic 9         -6.037899             -14.226866   \n",
      "\n",
      "   operating_systems  algorithm_design  computer_programming  \\\n",
      "0          -5.995275         -6.687968            -13.691942   \n",
      "1         -13.671018         -7.963907            -13.671018   \n",
      "2          -7.600121        -13.998716             -7.312855   \n",
      "3         -13.462488         -6.776627            -13.462488   \n",
      "4         -13.772562        -13.772562             -7.778601   \n",
      "5         -13.626238         -6.940377             -4.499171   \n",
      "6         -14.037662         -6.030962            -14.037662   \n",
      "7          -4.558467        -13.507573            -13.507573   \n",
      "8          -5.992611         -6.022455            -14.124436   \n",
      "9         -14.226866        -14.226866            -14.226866   \n",
      "\n",
      "   network_security  computer_graphics  software_engineering  cryptography  \\\n",
      "0        -13.691942          -6.521053            -13.691942    -13.691942   \n",
      "1         -6.762263          -7.118510            -13.671018     -3.818771   \n",
      "2        -13.998716          -6.620332             -4.369599    -13.998716   \n",
      "3         -4.868148          -7.468526             -6.458513     -7.468526   \n",
      "4        -13.772562         -13.772562            -13.772562    -13.772562   \n",
      "5        -13.626238         -13.626238             -7.073730    -13.626238   \n",
      "6        -14.037662         -14.037662            -14.037662    -14.037662   \n",
      "7         -5.810906         -13.507573             -6.503599     -7.513611   \n",
      "8         -6.260785         -14.124436            -14.124436    -14.124436   \n",
      "9        -14.226866          -7.135956            -14.226866    -14.226866   \n",
      "\n",
      "   data_structures  \n",
      "0        -4.692199  \n",
      "1       -13.671018  \n",
      "2        -7.089961  \n",
      "3       -13.462488  \n",
      "4        -6.276465  \n",
      "5        -6.717483  \n",
      "6        -6.064852  \n",
      "7        -7.513611  \n",
      "8        -6.523034  \n",
      "9       -14.226866  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-834-b0d704f4c5c1>:3: DeprecationWarning: invalid escape sequence \\c\n",
      "  df_relevance_2=pd.DataFrame(topics,columns=['topics\\core concepts'])\n"
     ]
    }
   ],
   "source": [
    "# Determining relevance of core concepts\n",
    "topics=['topic 0','topic 1','topic 2','topic 3','topic 4','topic 5','topic 6','topic 7','topic 8','topic 9']\n",
    "df_relevance_2=pd.DataFrame(topics,columns=['topics\\core concepts'])\n",
    "for core_concept in core_concepts:\n",
    "    index=word2id[core_concept]\n",
    "    df_relevance_2.insert(1,core_concept,relevance_2.loc[index])\n",
    "print(df_relevance_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Filtering Terms according to their relevance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 835,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 'analysis'], [1, 'artificial'], [2, 'artificial_bee_colony'], [3, 'color_information'], [4, 'cross_validation'], [5, 'explaining'], [6, 'handle'], [7, 'increase'], [9, 'method'], [10, 'neural_networks'], [11, 'novel'], [12, 'objective'], [13, 'principal_component'], [14, 'proposed_method'], [15, 'recall_rate'], [16, 'repetition'], [17, 'result'], [18, 'state-of-the-art'], [19, 'support_vector_machines'], [20, 'total'], [21, 'vector_machine'], [22, 'vision'], [24, 'clinical_data'], [25, 'graphic'], [27, 'movement'], [28, 'present_study'], [29, 'purpose'], [30, 'quantify'], [32, 'subject'], [34, 'viewing'], [35, 'virtual'], [36, 'amounts'], [38, 'data_entry'], [39, 'example'], [41, 'extraction'], [42, 'implement'], [43, 'input'], [44, 'limitation'], [45, 'link'], [46, 'management_system'], [47, 'moving'], [48, 'occupancy'], [49, 'operating'], [50, 'outside'], [51, 'package'], [53, 'processing'], [54, 'record'], [55, 'researcher'], [56, 'spatial'], [57, 'spatial_distribution'], [59, 'toolbox'], [60, '2016'], [61, 'catchment'], [62, 'characteristic'], [63, 'conservation'], [64, 'copyright'], [65, 'cross'], [66, 'discover'], [67, 'entropy'], [68, 'geology'], [70, 'guide'], [72, 'john_wiley'], [73, 'land_use'], [74, 'learning_approach'], [75, 'ltd.'], [76, 'machine'], [77, 'maxima'], [78, 'modify'], [79, 'occurrence'], [80, 'percentage'], [81, 'prediction_accuracy'], [82, 'sample_size'], [83, 'sons'], [84, 'stream'], [85, 'suitability'], [86, 'variable'], [87, 'advantage'], [88, 'analyse'], [90, 'data_source'], [91, 'facilitates'], [92, 'growing_number'], [93, 'organisms'], [94, 'phylogenetics'], [96, 'present'], [98, 'uses'], [100, 'cause'], [101, 'create'], [102, 'existing_methods'], [104, 'future'], [105, 'lower_cost'], [106, 'mean'], [107, 'mechanism'], [108, 'mining'], [110, 'operation'], [111, 'price'], [112, 'quantity'], [113, 'rate'], [114, 'reduces'], [116, 'running'], [117, 'securing'], [118, 'distribution'], [119, 'gamma'], [120, 'image_processing'], [121, 'measure'], [123, 'probe'], [125, 'technique'], [126, 'transition'], [127, 'consists'], [128, 'image_information'], [130, 'practicality'], [131, 'product'], [133, 'research_field'], [134, 'visualization_technology'], [137, 'effective'], [138, 'finite_difference'], [139, 'finite_element'], [140, 'major_advantage'], [142, 'outer_region'], [143, 'parallel'], [144, 'parallel_scalability'], [145, 'produce'], [148, 'diverse_set'], [149, 'elsevier_inc._all_rights'], [150, 'experimental_result'], [151, 'inaccuracy'], [152, 'pose'], [153, 'prior'], [155, 'propose'], [156, 'scene'], [157, 'single_image'], [158, 'test'], [159, 'based'], [160, 'clustering_technique'], [161, 'dual'], [162, 'element'], [163, 'engineering'], [165, 'fuzzy'], [166, 'matching'], [167, 'outperforms'], [168, 'patch'], [169, 'reverse'], [170, 'term'], [172, 'assess'], [173, 'benefit'], [174, 'compare'], [175, 'computer'], [176, 'conduct'], [177, 'e.g.'], [180, 'opinion'], [181, 'screen'], [182, 'snapshot'], [184, 'watermarking'], [185, '3d_model'], [186, 'advancement'], [187, 'based_technique'], [188, 'construct'], [189, 'data'], [190, 'detail'], [191, 'disadvantage'], [192, 'feasible'], [193, 'format'], [194, 'image'], [195, 'model'], [197, 'outcome'], [199, 'printer'], [200, 'reconstruction'], [201, 'reverse_engineering'], [202, 'design_model'], [203, 'gesture'], [207, 'user'], [208, 'user_interaction'], [209, 'user_interface'], [210, 'ability'], [211, 'benchmark'], [212, 'extend'], [213, 'participant'], [214, 'retrieval_algorithm'], [216, 'training_data'], [217, 'training_set'], [218, 'adaptation'], [219, 'consideration'], [222, 'potential'], [223, 'proposal'], [224, 'sample_data'], [225, '2015'], [226, 'aims'], [227, 'battery_life'], [228, 'bottleneck'], [229, 'delegation'], [230, 'demonstrates'], [234, 'mobile_user'], [235, 'offer'], [237, 'period'], [241, 'signing'], [243, 'applicability'], [244, 'beta'], [246, 'constructing'], [247, 'efficient_algorithm'], [248, 'epsilon'], [249, 'exhibit'], [250, 'fundamental_problem'], [251, 'graph_algorithm'], [252, 'notion'], [253, 'numerous_applications'], [254, 'pair'], [255, 'publication'], [256, 'resource'], [257, 'setting'], [258, 'shortest_path'], [259, 'vast_range'], [260, 'vertex'], [261, 'exploit'], [262, 'lifetime'], [263, 'low_power'], [264, 'machine-learning'], [265, 'many-core'], [266, 'meeting'], [270, 'computational'], [273, 'relationship'], [274, 'return'], [276, 'traffic'], [277, 'utility'], [279, 'capture'], [280, 'characterizing'], [281, 'class'], [282, 'defines'], [283, 'elsevier_b.v._all_rights'], [284, 'essence'], [285, 'extension'], [287, 'satisfies'], [288, 'sequential'], [289, 'update'], [291, 'adversary'], [292, 'embedding'], [293, 'fact'], [294, 'filtering'], [295, 'peak'], [296, 'proposed_technique'], [297, 'robust'], [298, 'spatial_domain'], [299, 'watermark'], [302, 'worst_case'], [304, 'euclidean'], [305, 'furthermore'], [307, 'machine_learning_algorithm'], [309, 'neighborhood'], [310, 'partial'], [311, 'query_point'], [312, 'reference'], [313, 'respect'], [314, 'algorithm'], [315, 'assignment'], [317, 'complexity'], [318, 'local'], [319, 'polynomial_time'], [320, 'search'], [321, 'search_algorithm'], [322, 'turn'], [323, '2017'], [326, 'defect'], [327, 'detection_algorithm'], [328, 'detection_methods'], [329, 'deviation'], [330, 'elsevier'], [332, 'online'], [333, 'presence'], [334, 'radii'], [335, 'sample'], [336, 'shape'], [339, 'validation'], [340, 'vision_system'], [342, 'adding'], [344, 'creating'], [346, 'judges'], [348, 'piece'], [350, 'bound'], [351, 'complement'], [352, 'connected_component'], [353, 'data_structures'], [354, 'et_al.'], [355, 'implementing'], [356, 'performing'], [357, 'recursive'], [359, 'experiments'], [360, 'fast'], [361, 'finally'], [362, 'fitting'], [363, 'generated_model'], [364, 'gradient'], [365, 'kinds'], [367, 'minute'], [368, 'requirement'], [369, 'visualization_method'], [371, 'assumption'], [372, 'black-box'], [373, 'complex_model'], [374, 'complex_task'], [376, 'decision_support'], [377, 'elsevier_ltd._all_rights'], [378, 'explanation'], [379, 'flexibility'], [381, 'inside'], [382, 'learning'], [384, 'novel_use'], [385, 'outline'], [386, 'possibility'], [387, 'prediction_model'], [388, 'recommendation'], [389, 'sale'], [390, 'separation'], [394, 'algorithms'], [395, 'attribute'], [396, 'capability'], [398, 'foci'], [399, 'information'], [400, 'knowledge'], [401, 'language'], [402, 'paper_presents'], [403, 'popularization'], [404, 'relational'], [405, 'series'], [406, 'single_type'], [407, 'cmos'], [410, 'gain'], [411, 'imaging'], [412, 'raspberry'], [413, 'real-world'], [414, 'reproduction'], [415, 'sensing'], [417, 'spie'], [418, 'the_raspberry_pi'], [419, 'account'], [420, 'average_time'], [421, 'classification_method'], [422, 'current'], [423, 'disturbance'], [424, 'fire_detection'], [425, 'kalman'], [427, 'resulting'], [428, 'sampling'], [429, 'segmenting'], [430, 'standard_deviation'], [431, 'suppression'], [433, 'cover'], [434, 'impact'], [435, 'minimizes'], [436, 'simple'], [437, 'time_consuming'], [438, 'algorithm_design'], [439, 'configuration'], [440, 'monitoring_system'], [441, 'proposed_model'], [442, 'risk'], [444, 'scoring'], [445, 'system_design'], [447, 'define'], [449, 'iterative_approach'], [450, 'optimal_solution'], [451, 'option'], [452, 'relation'], [453, 'representative'], [454, 'restriction'], [455, 'aspect'], [457, 'classification_model'], [458, 'cross-validation'], [460, 'elsevier_b.v.'], [463, 'split'], [464, 'test_set'], [465, 'unsupervised_learning'], [466, 'data_fusion'], [467, 'duplicate'], [468, 'experimental'], [470, 'priori'], [475, 'multi-agent'], [476, 'organization'], [478, 'tuning'], [479, 'usefulness'], [480, 'accurate_prediction'], [481, 'based_algorithm'], [482, 'computer_vision'], [483, 'damage_detection'], [484, 'identifying'], [485, 'improvement'], [486, 'integrating'], [487, 'laser'], [488, 'measurement'], [490, 'non-contact'], [491, 'optical_flow'], [492, 'relative'], [493, 'skew'], [494, 'stiffness'], [495, 'structural'], [497, 'structural_properties'], [498, 'system_identification'], [499, 'video'], [501, 'attempt'], [502, 'evaluation_result'], [504, 'incoming_packet'], [505, 'intend'], [506, 'internet'], [507, 'internet_users'], [508, 'malicious_activities'], [509, 'malicious_packets'], [511, 'sender'], [513, 'tactics'], [514, 'computer_program'], [515, 'database_management'], [516, 'examples'], [517, 'multiple_tables'], [520, 'execution'], [523, 'instance'], [526, 'nsga-ii'], [527, 'antenna'], [528, 'lead'], [529, 'main_reason'], [530, 'operating_systems'], [531, 'technical_capabilities'], [533, 'boundary_condition'], [534, 'core'], [535, 'discrete'], [539, 'magnitude'], [540, 'nature'], [541, 'open_source'], [543, 'parallel_computing'], [549, 'test_case'], [551, 'basic_idea'], [552, 'convergence'], [553, 'decomposition_method'], [554, 'numerical_experiments'], [555, 'solving'], [557, 'detecting'], [559, 'flash'], [561, 'paper_investigates'], [563, 'automation'], [566, 'open-source'], [567, 'opencv'], [568, 'conjunction'], [569, 'modified_version'], [571, 'square'], [572, 'domain'], [574, 'computed_tomography'], [577, 'durability'], [578, 'fuel'], [581, 'thinning'], [582, 'trend'], [584, 'challenge'], [586, 'current_state'], [588, 'dictionary_learning'], [589, 'network_connectivity'], [590, 'novel_approach'], [591, 'scientific_research'], [592, 'signal_processing'], [593, 'software_design'], [595, 'time-frequency_analysis'], [596, 'vary'], [598, 'available_data'], [600, 'condition'], [601, 'data_set'], [602, 'foreground'], [603, 'ground_truth'], [604, 'learning_algorithm'], [605, 'machine_learning'], [606, 'pixel'], [607, 'representation'], [608, 'shadow'], [609, 'skin'], [611, 'favor'], [613, 'key_concept'], [614, 'ontology'], [615, 'proposed_architecture'], [618, 'visitors'], [619, 'concordance'], [621, 'influence'], [622, 'brightness'], [623, 'certain_degree'], [624, 'complex'], [625, 'ground'], [627, 'light'], [628, 'light_source'], [629, 'machine_vision'], [632, 'relationship_model'], [634, 'roughness'], [636, 'authentication_protocol'], [641, 'identifies'], [645, 'lightweight'], [647, 'remedy'], [649, 'secure'], [652, 'rgb_values'], [653, 'cardinality'], [655, 'fraction'], [656, 'frequency'], [658, 'procedure'], [659, 'property'], [660, 'sparse_set'], [661, 'time'], [662, 'china'], [663, 'days'], [664, 'degrade'], [666, 'loss'], [667, 'missing'], [669, 'concern'], [670, 'content'], [671, 'criteria'], [672, 'decision_tree'], [673, 'elsevier_ltd.'], [676, 'learning_principle'], [677, 'personnel'], [679, 'real_world'], [682, 'temperature'], [684, 'wheel'], [685, 'artificial_neural_networks'], [686, 'character'], [689, 'following'], [690, 'help'], [695, 'three-layer'], [696, 'carbon'], [698, 'climate_change'], [699, 'column'], [700, 'contributing'], [701, 'high-resolution'], [702, 'large-scale'], [703, 'mixing'], [706, 'developed_model'], [707, 'extreme'], [709, 'making'], [711, 'remote_sensing_data'], [712, 'room'], [714, 'total_number'], [715, 'key_aspect'], [717, 'network_performance'], [718, 'optimal'], [719, 'planning'], [720, 'secure_system'], [721, 'security'], [723, 'choosing'], [724, 'coefficient'], [725, 'estimation_accuracy'], [726, 'grade'], [728, 'key_issue'], [730, 'reason'], [731, 'sorting'], [732, 'system'], [734, 'variety'], [736, 'control'], [737, 'floating_point'], [739, 'ieee'], [740, 'look'], [742, 'slow'], [743, 'systems'], [744, 'latent_variable'], [747, 'yield'], [748, 'american_society'], [750, 'california'], [751, 'civil'], [752, 'construction'], [753, 'error'], [754, 'input_variable'], [755, 'installation'], [757, 'linear_regression'], [758, 'multiple'], [760, 'predictive'], [761, 'proposed_methodology'], [762, 'span'], [763, 'statistical_inference'], [765, 'university'], [766, 'back'], [769, 'variant'], [770, 'approximation_algorithm'], [771, 'communication_cost'], [772, 'copy'], [773, 'distributed'], [775, 'list'], [776, 'meet'], [777, 'np-hard'], [778, 'optimize'], [781, 'differentiation'], [782, 'image_segmentation'], [783, 'main_contribution'], [785, 'processing_capabilities'], [786, 'ways'], [787, 'developed_system'], [790, 'matlab'], [791, 'obtaining'], [792, 'penetration'], [794, 'reduction'], [795, 'relative_error'], [796, 'subtraction'], [799, 'bridge'], [801, 'exponent'], [802, 'lyapunov'], [806, 'phenomena'], [807, 'potential_application'], [808, 'easy'], [809, 'images'], [810, 'smartphone'], [812, 'support'], [813, 'vector'], [814, 'form'], [816, 'slice'], [818, 'forecast'], [819, 'national_center'], [820, 'photovoltaic'], [821, 'plant'], [822, 'power_plant'], [823, 'ranging'], [825, 'processing_data'], [826, 'science'], [827, 'avoids'], [829, 'genetic_algorithms'], [830, 'micro'], [832, 'search_space'], [834, 'analysis_result'], [835, 'battery'], [836, 'experimental_testing'], [837, 'mechanical_properties'], [838, 'perspective'], [839, 'supply'], [841, 'finite'], [842, 'grain'], [844, 'interconnects'], [846, 'parallel_computer'], [847, 'statistics'], [849, 'alternative'], [851, 'differs'], [853, 'effective_tool'], [855, 'enabling'], [856, 'facility'], [857, 'function'], [859, 'prediction_error'], [861, 'constant'], [862, 'dynamic_process'], [863, 'round'], [865, 'welding'], [867, 'data_synchronization'], [869, 'mission'], [870, 'relational_databases'], [871, 'analyzing'], [872, 'computational_efficiency'], [873, 'interconnect'], [874, 'large-scale_problem'], [878, 'stress'], [880, 'fluid'], [883, 'coding'], [884, 'experiment'], [885, 'fitness_function'], [887, 'distributed_environment'], [888, 'memory_requirement'], [889, 'memory_usage'], [890, 'multitude'], [893, 'signs'], [894, 'display'], [895, 'light_field'], [896, 'moves'], [897, 'zones'], [898, 'network_security'], [903, 'efficacy'], [904, 'effort'], [905, 'heterogeneous'], [909, 'networking'], [910, 'particle'], [911, 'particle_swarm_optimization'], [912, 'scientific_computing'], [913, 'swarm_optimization'], [914, 'training'], [915, 'complementarity'], [916, 'contact'], [917, 'finite_element_method'], [918, 'forming'], [919, 'freedom'], [922, 'iterate'], [923, 'iteration'], [925, 'coordinate'], [927, 'avalanche'], [928, 'brute_force'], [935, 'shuffling'], [936, 'text'], [937, 'data-driven_model'], [938, 'paradigm'], [939, 'particle_size_distribution'], [941, 'regression_model'], [942, 'assessment'], [943, 'error_rate'], [944, 'monitor'], [945, 'relative_difference'], [946, 'satellite'], [947, 'site'], [948, 'variation'], [951, 'angle'], [952, 'disk'], [954, 'fall'], [956, 'paramount_importance'], [958, 'atom'], [959, 'brute-force'], [960, 'collection'], [961, 'computing_performance'], [962, 'couple'], [963, 'high-performance'], [967, 'games'], [971, 'significant_amount'], [973, 'average_value'], [979, 'recall'], [980, 'smart_device'], [981, 'smart_phone'], [982, 'smartphone_application'], [985, 'cloud_service'], [987, 'developer'], [988, 'distributed_file_system'], [989, 'fault_tolerance'], [992, 'storage_system'], [994, 'stored_data'], [995, 'equipment'], [996, 'evaluation_method'], [997, 'integral'], [998, 'probability_distribution'], [1000, 'stroke'], [1006, 'tlbo'], [1007, 'feature_space'], [1010, 'particular_value'], [1011, 'preserves'], [1013, 'projection'], [1015, 'compared'], [1016, 'counterpart'], [1022, 'conventional'], [1024, 'correlation_coefficient'], [1025, 'increasing'], [1031, 'high-speed'], [1032, 'major_obstacle'], [1033, 'measuring'], [1034, 'tooling'], [1038, 'current_status'], [1040, 'linux'], [1041, 'software_package'], [1042, 'software_system'], [1043, 'start'], [1045, 'upgrade'], [1046, 'derivation'], [1047, 'hyperspectral_imaging'], [1048, 'simplicity'], [1050, 'comparisons'], [1051, 'fruit'], [1052, 'multi-level'], [1054, 'visualisation'], [1055, 'x-ray_ct'], [1056, 'data_retrieval'], [1057, 'data_table'], [1058, 'data_warehouse'], [1059, 'database_schema'], [1061, 'hadoop'], [1062, 'hbase'], [1063, 'photovoltaics'], [1065, 'contrary'], [1066, 'data_storage'], [1067, 'demonstration'], [1070, 'mongodb'], [1071, 'paper_examines'], [1072, 'priority'], [1075, 'security_measure'], [1076, 'security_problem'], [1079, 'consensus'], [1082, 'desired_properties'], [1083, 'guarantee'], [1085, 'international_conference'], [1086, 'natural_extension'], [1087, 'principle'], [1090, 'relies'], [1091, 'sense'], [1092, 'stop'], [1093, 'system_model'], [1094, 'universal'], [1097, 'encoding_scheme'], [1099, 'exchange'], [1100, 'proposed_scheme'], [1102, 'calibration'], [1103, 'lens'], [1104, 'liquid'], [1105, 'data_points'], [1106, 'base_station'], [1107, 'cloud'], [1109, 'clustering_process'], [1111, 'iterative'], [1112, 'numerical_simulation_results'], [1113, 'original_problem'], [1114, 'significant_improvement'], [1115, 'weighted'], [1116, 'bidirectional_reflectance_distribution_function'], [1118, 'minimisation'], [1119, 'switching'], [1120, 'visual_database'], [1121, 'age_estimation'], [1123, 'computer_vision_problems'], [1124, 'crowd'], [1125, 'head'], [1126, 'low-level'], [1127, 'manual_work'], [1128, 'visual_attribute'], [1129, 'algebraic'], [1131, 'iterative_method'], [1134, 'previous_study'], [1136, 'communications_technology'], [1138, 'security_protocols'], [1139, 'dynamic_programming_algorithm'], [1140, 'optimization'], [1141, 'original'], [1144, 'equation'], [1145, 'excellent'], [1147, 'initial_value'], [1148, 'integration_methods'], [1149, 'integrator'], [1152, 'partial_differential_equation'], [1153, 'pdes'], [1154, 'scaling'], [1155, 'waveform'], [1156, 'correlation_analysis'], [1157, 'spatial_resolution'], [1158, 'based_method'], [1160, 'compact'], [1161, 'match'], [1162, 'motivate'], [1163, 'special_form'], [1164, 'alarm'], [1165, 'distinction'], [1167, 'feature_set'], [1169, 'strategy'], [1175, 'calculation_method'], [1176, 'perspective_projection'], [1179, 'radio_frequency'], [1180, 'simulation_model'], [1181, 'simulation_result'], [1184, 'agent'], [1186, 'alternative_approach'], [1187, 'design'], [1188, 'examines'], [1189, 'ontologies'], [1190, 'plethora'], [1192, 'unification'], [1194, 'hardware_platform'], [1196, 'lighting'], [1197, 'low_cost'], [1198, 'open_source_library'], [1200, 'play'], [1201, 'reach'], [1202, 'sound'], [1203, 'consumer'], [1207, 'nets'], [1210, 'rating'], [1211, 'share'], [1213, 'circulation'], [1216, 'earth'], [1219, 'open'], [1222, 'roof'], [1223, 'symbol'], [1225, 'conventional_relational_databases'], [1230, 'query'], [1232, 'serve'], [1238, 'study_area'], [1239, 'treatment'], [1240, 'entry'], [1242, 'overlapping'], [1244, 'equality'], [1245, 'regard'], [1246, 'stakeholder'], [1250, 'cpus'], [1251, 'essential_feature'], [1252, 'future_direction'], [1253, 'gpus'], [1254, 'high-performance_parallel_computing'], [1255, 'landscape'], [1257, 'open_source_software'], [1258, 'opencl'], [1259, 'physical_properties'], [1261, 'prerequisite'], [1262, 'constructive'], [1265, 'arbitrary'], [1266, 'based_approach'], [1267, 'conditioning'], [1268, 'constraints'], [1269, 'functional_dependency'], [1270, 'probabilistic'], [1271, 'probabilistic_database'], [1272, 'subsequent_query'], [1273, 'duration'], [1276, 'pareto'], [1278, 'particle_swarm_algorithm'], [1280, 'project_manager'], [1283, 'buffer'], [1284, 'checking'], [1285, 'compilation'], [1288, 'execute'], [1289, 'instruction'], [1290, 'logic'], [1291, 'past'], [1293, 'threat'], [1294, 'arduino'], [1295, 'circuit_design'], [1297, 'details'], [1298, 'door'], [1301, 'management'], [1302, 'mobile_app'], [1303, 'relay'], [1304, 'school_students'], [1307, 'development_platform'], [1311, 'jitter'], [1312, 'present_article'], [1317, 'states'], [1318, 'competitiveness'], [1319, 'game'], [1321, 'randomized_algorithm'], [1322, 'controlling'], [1323, 'enhancement'], [1324, 'fingerprint'], [1325, 'gaussian'], [1326, 'hardware_architecture'], [1328, 'input_image'], [1330, 'test_result'], [1331, 'intuitive'], [1332, 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'value'], [6858, 'blurring'], [6862, \"user's_privacy\"], [6865, 'brief'], [6866, 'coefficient_matrix'], [6872, 'q-learning'], [6878, 'media'], [6879, 'reality_technology'], [6881, 'euclidean_plane'], [6885, 'underlying_data_structures'], [6890, 'queueing'], [6895, 'entanglement_evolution'], [6898, 'real-time_analysis'], [6900, 'clinical_application'], [6917, 'legitimate_user'], [6918, 'wireless_communication_system'], [6919, 'penalty'], [6923, 'hybrid_reality'], [6935, 'creator'], [6946, 'fuzzy_inference_system'], [6950, 'software_defined_networking'], [6956, 'mesh'], [6960, 'residents'], [6963, 'model_checker'], [6966, 'dual_problem'], [6975, 'significant_performance_gain'], [6976, 'spanning_tree'], [6977, 'upper_bound_k'], [6980, 'agent-based_modeling'], [6981, 'scanning'], [6982, 'transition_zones'], [6992, 'amplify'], [6996, 'degrees_c.'], [7003, 'stationary_solution'], [7008, 'common_task'], [7011, 'amplifier'], [7018, 'detailed_performance_analysis'], [7019, 'memetic_algorithm'], [7026, 'scheme'], [7033, 'pipe'], [7034, 'matrix_operations'], [7036, 'access_control_system'], [7037, 'batch_processing'], [7040, 'visual_secret_sharing_scheme'], [7047, 'incomplete_information'], [7050, 'representation_system'], [7054, 'mac_os_x'], [7055, 'collocation'], [7057, 'industrial'], [7069, 'intercept'], [7074, 'united_states'], [7076, 'source'], [7078, 'innovation'], [7081, 'travel_time'], [7084, 'conversation'], [7097, 'accountability'], [7106, 'stealthiness'], [7116, 'strong_candidate'], [7120, 'related_parameters'], [7121, 'control_signal'], [7132, 'danger'], [7133, 'essential_part'], [7136, 'functioning'], [7138, 'essential_element'], [7143, 'limited_set'], [7144, 'proceeds'], [7146, 'indexing'], [7150, 'belief'], [7151, 'hidden_behavior'], [7154, 'scientific_disciplines'], [7158, 'following_question'], [7165, 'theoretical_basis'], [7167, 'combinatorial_optimization_problem'], [7168, 'data_sample'], [7172, 'encapsulation'], [7174, 'retrieval'], [7176, 'folksonomies'], [7178, 'threats'], [7179, 'proximity'], [7182, 'instability'], [7190, 'common_object'], [7197, 'distributed_computing_architecture'], [7199, 'activation'], [7200, 'graph_analysis'], [7202, 'growing_popularity'], [7208, 'diffusion_coefficient'], [7214, 'ramanujan'], [7215, 'ranking_function'], [7220, 'preference'], [7222, 'industrial_applications'], [7229, 'schema'], [7231, 'bucket'], [7236, 'function_calls'], [7240, 'yuan_et_al.'], [7241, 'recipient'], [7246, 'lots'], [7249, 'american_association'], [7255, 'night'], [7256, 'macroscopic'], [7264, 'cyber-physical_systems'], [7267, 'negative_effect'], [7278, 'neutron'], [7279, 'promising_future'], [7284, 'consumer_electronics'], [7285, 'classifies'], [7286, 'significant_performance'], [7292, 'component_model'], [7297, 'density_field'], [7298, 'summation'], [7301, 'decomposes'], [7302, 'scarcity'], [7309, 'attraction'], [7310, 'basin'], [7321, 'outdoor_scenes'], [7324, 'partial_results'], [7326, 'hmds'], [7328, 'promising_direction'], [7329, 'elsevier_gmbh.'], [7333, 'quality_improvement'], [7340, 'application_program_interface'], [7344, 'specific_requirements'], [7349, 'conventions'], [7350, 'editor'], [7352, 'message-passing'], [7358, 'mobile_computing'], [7364, 'service_layer'], [7370, 'close_proximity'], [7375, 'access'], [7378, 'static_approach'], [7379, 'azimuthal'], [7383, 'executable_code'], [7385, 'final_system'], [7386, 'main_principle'], [7387, 'search_operation'], [7395, 'visual_representation'], [7396, 'radiative_transfer'], [7398, 'modern'], [7400, 'processing_steps'], [7403, 'hood'], [7404, 'novel_mechanism'], [7412, 'multinomial'], [7414, 'standard_scheme'], [7416, 'track'], [7418, 'redundancy'], [7420, 'parallelization_method'], [7424, 'mathematical_properties'], [7426, 'mosquitoes'], [7432, 'spatial_discretization'], [7435, 'hardest_problems'], [7445, 'importance_sampling'], [7451, 'movement_data'], [7456, 'disk_space'], [7460, 'target_application'], [7474, 'label_ranking'], [7476, 'system_component'], [7482, 'molecular_dynamics'], [7485, 'wang_et_al.'], [7488, 'talk'], [7494, 'nics'], [7495, 'load-balancing'], [7498, 'major_limitation'], [7500, 'analyzer'], [7501, 'honeynet'], [7502, 'software-defined_networking'], [7504, 'broad_set'], [7507, 'mixture'], [7511, 'automated_image_analysis'], [7513, 'nominal_data'], [7516, 'nonlinear'], [7521, 'underlying_structure'], [7528, 'restful_api'], [7529, 'atomicity'], [7530, 'consistency'], [7531, 'memory_architecture'], [7534, 'repertoire'], [7535, 'fear'], [7536, 'integrity'], [7539, 'software_program'], [7540, 'key_part'], [7541, 'smart_factory'], [7545, 'arrival'], [7548, 'smart_tvs'], [7551, 'current_literature'], [7553, 'orthographic_projection'], [7556, 'laboratory'], [7560, 'socket'], [7561, 'information_and_communication_technology'], [7563, 'scaling_factor'], [7565, 'deep_understanding'], [7573, 'radiologist'], [7574, 'rational_use'], [7575, 'safe_algorithm'], [7576, 'visit'], [7579, 'voxel'], [7581, 'visual_fatigue'], [7586, 'genetic_engineering'], [7588, 'computer-based_system'], [7592, 'quadratic_equations'], [7602, 'biggest_challenge'], [7616, 'token'], [7620, 'practical_problem'], [7622, 'intelligent_vehicle'], [7623, 'pattern_classification'], [7640, 'knowledge_gap'], [7643, 'orientation'], [7646, 'school'], [7649, 'doubt'], [7655, 'unique_opportunity'], [7657, 'ubiquity'], [7659, 'ceiling'], [7660, 'cpu_cores'], [7670, 'confidence_level'], [7671, 'peer'], [7678, 'asphalt'], [7684, 'pevs'], [7686, 'system_size'], [7691, 'portfolio'], [7693, 'wind_power'], [7695, 'lower_frequency'], [7696, 'geometrical_characteristics'], [7697, 'spacing'], [7708, 'cavities'], [7711, 'certain_amount'], [7712, 'multiple_threads'], [7716, 'information_integration'], [7734, 'antarctica'], [7738, 'curriculum'], [7746, 'dementia'], [7748, 'fisher'], [7749, 'manufacturer'], [7756, 'stacking'], [7760, 'turbidity'], [7764, 'hydrogen'], [7765, 'object-oriented_program'], [7767, 'construction_site'], [7775, 'fundamental_mechanism'], [7780, 'representative_set'], [7787, 'discriminant_feature'], [7789, 'client_side'], [7791, 'lookup_table'], [7792, 'multi-threading'], [7796, 'huge_number'], [7798, 'cloud_server'], [7804, 'illusion'], [7805, 'industrial_project'], [7809, 'opensees'], [7810, 'mathematical_programming'], [7813, 'relevant_literature'], [7818, 'scalable_system'], [7821, 'nanotechnology'], [7824, 'scripting_language'], [7828, 'ancilla'], [7831, 'iran'], [7832, 'orthopedic_surgeons'], [7833, 'assembles'], [7835, 'prey'], [7841, 'essential_role'], [7842, 'simplified_version'], [7847, 'money'], [7852, 'restart'], [7856, 'study_group'], [7860, 'interactive_simulation'], [7861, 'cryptography_algorithms'], [7864, 'affinity'], [7878, 'underlying_idea'], [7880, 'fibre'], [7890, 'analogue'], [7895, 'phase_ii'], [7900, 'research_team'], [7904, 'harmony'], [7909, 'proper_choice'], [7911, 'proof'], [7912, 'mathematical_modeling'], [7920, 'predictive_power'], [7921, 'wide_class'], [7925, 'quaternion'], [7927, 'visual_vocabulary'], [7931, 'cyberinfrastructure'], [7933, 'artefact'], [7937, 'active_system'], [7944, 'r-tree'], [7949, 'dramatic_growth'], [7950, 'coloring_algorithm'], [7957, 'efficient_computation'], [7960, 'description_method'], [7964, 'portable_device'], [7967, 'electromagnetic_wave'], [7975, 'user-friendly_graphical_interface'], [7976, 'transmitted_data'], [7978, 'interconnection'], [7982, 'upper_and_lower_bounds'], [7984, 'catalogue'], [7988, 'biologists'], [7993, 'annihilation'], [7994, 'institute'], [7997, 'clearance'], [7999, 'average'], [8002, 'distribution_parameter'], [8003, 'plastic'], [8004, 'nonlinear_dynamical_systems'], [8006, 'complete_binary_tree'], [8009, 'object_properties'], [8017, 'kinetic_energy'], [8018, 'debate'], [8027, 'border'], [8031, 'proposed_pipeline'], [8034, 'pump'], [8036, 'current_technology'], [8048, 'null_value'], [8050, 'traditional_implementation'], [8057, 'linked_open_data'], [8060, 'cent'], [8062, 'nearest_neighbor_algorithm'], [8065, 'real_world_data'], [8066, 'computer_data'], [8069, 'genus'], [8075, 'distributed_manner'], [8078, 'legendre'], [8079, 'problem_data'], [8080, 'lbp_codes'], [8095, 'mapping_process'], [8105, 'forging'], [8107, 'electric_energy'], [8108, 'powertrain_system'], [8110, 'adjacency'], [8111, 'brevity'], [8114, 'marker'], [8117, 'biomass'], [8121, 'surgery'], [8123, 'tunnelling'], [8127, 'linearizability'], [8130, 'primary_goal'], [8132, 'equilibrium_computation'], [8144, 'archived_data'], [8148, 'basic_characteristics'], [8156, 'technical_issues'], [8168, 'distributed_algorithm_design'], [8169, 'step'], [8171, 'inventory_control'], [8188, 'coordination'], [8194, 'discrete_system'], [8204, 'rule-based_approach'], [8205, 'stripe'], [8214, 'vane'], [8219, 'guis'], [8229, 'shopping'], [8233, 'warning_system'], [8234, 'surgical_training'], [8236, 'stochastic_programming'], [8240, 'cs_plugged'], [8241, 'cs_unplugged'], [8245, 'printing_technology']]\n"
     ]
    }
   ],
   "source": [
    "low_relevance_terms=[]\n",
    "low_relevance_ids=[]\n",
    "for i in range(len(vocab)):\n",
    "    term_relevance=relevance_0.loc[i]\n",
    "    if (max(term_relevance)<2):\n",
    "        low_relevance_ids.append(i)\n",
    "        low_relevance_terms.append(id2word[i])\n",
    "print(low_relevance_terms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 836,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3738\n"
     ]
    }
   ],
   "source": [
    "print(len(low_relevance_terms))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 837,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2901\n"
     ]
    }
   ],
   "source": [
    "count=0\n",
    "for term in low_relevance_terms:\n",
    "    term=term[1]\n",
    "    labels=labeled_corpus['label']\n",
    "    terms=labeled_corpus['term']\n",
    "    terms=list(terms)\n",
    "    id=terms.index(term)\n",
    "    if(labels[id]=='o'):\n",
    "        count=count+1\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 838,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filter Out of the Dictionary\n",
    "dictionary.filter_tokens(bad_ids=low_relevance_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 839,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab=tuple(dictionary[i] for i in range(len(dictionary)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 840,
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute term counts in text\n",
    "word_counts=[]\n",
    "for term in vocab:\n",
    "    i=terms_list.index(term)\n",
    "    word_counts.append(freq[i])\n",
    "#compute number of documents that contain each term\n",
    "doc_term_counts=[]\n",
    "for i in range(len(dictionary)):\n",
    "    count=0\n",
    "    for j in range(len(docs)):\n",
    "        if (dtm[j][i]!=0):\n",
    "            count=count+1\n",
    "    doc_term_counts.append(count)\n",
    "doc_term_counts=np.array(doc_term_counts)\n",
    "#computing tf-idf after filtering\n",
    "new_tfidf=[]\n",
    "for i in range(len(dictionary)):\n",
    "    new_tfidf.append((word_counts[i]*len(docs))/(len(dictionary)*doc_term_counts[i]))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 841,
   "metadata": {},
   "outputs": [],
   "source": [
    "#computing new document-term matrix\n",
    "new_dtm=[]\n",
    "for i in range(len(new_docs)):\n",
    "  doc_counts=[]\n",
    "  for j in range(len(dictionary)):\n",
    "    if (dictionary[j] in new_docs[i]):\n",
    "      doc_counts.append(1)\n",
    "    else:\n",
    "      doc_counts.append(0)\n",
    "  new_dtm.append(doc_counts)\n",
    "new_dtm=np.array(new_dtm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 842,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:lda:n_documents: 5746\n",
      "INFO:lda:vocab_size: 4523\n",
      "INFO:lda:n_words: 21827\n",
      "INFO:lda:n_topics: 10\n",
      "INFO:lda:n_iter: 100\n",
      "WARNING:lda:all zero row in document-term matrix found\n",
      "INFO:lda:<0> log likelihood: -319682\n",
      "INFO:lda:<20> log likelihood: -201237\n",
      "INFO:lda:<40> log likelihood: -196369\n",
      "INFO:lda:<60> log likelihood: -194239\n",
      "INFO:lda:<80> log likelihood: -193478\n",
      "INFO:lda:<99> log likelihood: -192191\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<lda.guidedlda.GuidedLDA at 0x1f6e02b1460>"
      ]
     },
     "execution_count": 842,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(new_dtm, seed_topics=seed_topics, seed_confidence=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 843,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:lda:all zero row in document-term matrix found\n"
     ]
    }
   ],
   "source": [
    "beta=model.topic_word_\n",
    "theta=model.transform(new_dtm)\n",
    "from sklearn import preprocessing\n",
    "normalized_theta=preprocessing.normalize(theta, norm='l2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 844,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data_structures': -0.01579600920086729, 'cryptography': -0.02091329102479285, 'software_engineering': -0.015019710010798883, 'computer_graphics': -0.004519270209490089, 'network_security': -0.012464423690572358, 'computer_programming': -0.01106581248443659, 'algorithm_design': -0.007732223866411531, 'operating_systems': -0.014474485455412816, 'distributed_computing': -0.004932256192377299, 'machine_learning': -0.004904565954170723}\n"
     ]
    }
   ],
   "source": [
    "for core_concept in core_concepts:\n",
    "    index=word2id[core_concept]\n",
    "    core_concepts_saliency[core_concept]=saliency[index]\n",
    "print(core_concepts_saliency)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**pyLDAviz**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 845,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el1272021583943260321899407939\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el1272021583943260321899407939_data = {\"mdsDat\": {\"x\": [0.06984892808410448, 0.04831364666895521, 0.031128019774022925, 0.1390927708503903, -0.1969162823453416, -0.25942484500187096, 0.16931073383601325, 0.11578624924007609, -0.26293073961967356, 0.14579151851332425], \"y\": [0.2440064850986587, 0.23326405931827543, 0.01242273224458875, -0.10775794155008597, -0.013115451862995143, -0.008167197770629766, -0.19876903513074576, 0.105077826913762, -0.1027462754237482, -0.16421520183707966], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [11.274589420840568, 11.255986768600348, 11.16702584247915, 10.83549578834935, 10.29957968966206, 10.169572353484915, 9.6513469871106, 9.168417836390347, 8.597618180572058, 7.580367132510599]}, \"tinfo\": {\"Term\": [\"software_engineering\", \"cryptography\", \"software\", \"computer_programming\", \"quanta\", \"scheduling\", \"quantum_cryptography\", \"skill\", \"encryption\", 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\"network_security_situation\", \"network_security_situation\", \"network_service\", \"network_service\", \"nids\", \"nonconvex\", \"novice\", \"novice\", \"object_classification\", \"operating_characteristic\", \"operating_characteristic\", \"optimization_process\", \"originality/value\", \"oscillation\", \"oscillator\", \"ownership\", \"ownership\", \"pairing\", \"pairing\", \"parallel_computation\", \"parallel_computation\", \"parallel_computation\", \"parallel_implementation\", \"parallel_implementation\", \"parallel_implementation\", \"parallel_version\", \"parallel_version\", \"parallel_version\", \"performance_requirement\", \"personality\", \"phone\", \"phone\", \"phone\", \"photo-optical_instrumentation_engineers\", \"physician\", \"physician\", \"plaintext\", \"plate\", \"plate\", \"point\", \"point_cloud\", \"point_cloud\", \"poisson\", \"pore\", \"pore\", \"posterior\", \"posterior_distribution\", \"power_analysis\", \"practitioner\", \"practitioner\", \"practitioner\", \"prediction_performance\", \"primary_studies\", \"prng\", \"problematic\", \"problematic\", \"product_line\", \"program\", \"program\", \"program_summary_program_title\", \"program_summary_url\", \"programming_concept\", \"programming_course\", \"programming_skills\", \"project_management\", \"proposed_design\", \"proposed_design\", \"proposed_protocol\", \"proposed_protocol\", \"pros\", \"psnr\", \"public-key\", \"public_datasets\", \"public_key\", \"public_key\", \"public_key_cryptography\", \"quality_attribute\", \"quanta\", \"quanta\", \"quantum_communication\", \"quantum_cryptography\", \"quantum_key_distribution\", \"quantum_key_distribution_protocol\", \"quantum_state\", \"queen's_university\", \"query_processing\", \"query_processing\", \"radio\", \"radio\", \"radio\", \"raid\", \"random_oracle\", \"rapid_advances\", \"rdbms\", \"reactor\", \"real-time_implementation\", \"real-time_monitoring\", \"real_scene\", \"recognition_accuracy\", \"reconstructed_image\", \"reconstruction_algorithm\", \"refractive_index\", \"regularization\", \"regularization\", \"regularization\", \"relevant_features\", \"requirement_specification\", \"requirements\", \"requirements\", \"requirements_engineering\", \"requirements_engineering\", \"research_effort\", \"research_effort\", \"research_effort\", \"research_limitations/implications\", \"reserve\", \"resource_allocation\", \"resource_allocation\", \"resource_allocation_algorithm\", \"resource_allocation_algorithm_design\", \"resource_provisioning\", \"restoration\", \"reusability\", \"rgb-d\", \"risk_management\", \"rmse\", \"rmse\", \"rmse\", \"rough_set_theory\", \"running_time\", \"runtime_performance\", \"scalability\", \"scalability\", \"scalar_multiplication\", \"scheduler\", \"scheduling\", \"scheduling\", \"scheduling\", \"scheduling\", \"scheduling_problem\", \"scheduling_strategies\", \"science_education\", \"scratch\", \"secret_image\", \"secret_key\", \"secret_key\", \"secret_key\", \"secret_key_rate\", \"secret_sharing\", \"security_aspect\", \"security_guarantee\", \"security_issue\", \"security_issue\", \"security_management\", \"security_management\", \"security_mechanism\", \"security_mechanism\", \"security_mechanism\", \"segmentation\", \"segmentation\", \"segmentation_algorithm\", \"segmentation_algorithm\", \"session_initiation\", \"session_key\", \"shape_model\", \"shape_model\", \"signature_scheme\", \"simd\", \"skill\", \"skill\", \"skill\", \"smoothness\", \"society\", \"software\", \"software\", \"software\", \"software_artifact\", \"software_development\", \"software_development\", \"software_development_process\", \"software_development_process\", \"software_development_project\", \"software_development_team\", \"software_engineer\", \"software_engineer\", \"software_engineering\", \"software_engineering\", \"software_engineering\", \"software_engineering\", \"software_engineering_problems\", \"software_engineering_process\", \"software_engineering_researchers\", \"software_industry\", \"software_industry\", \"software_metrics\", \"software_metrics\", \"software_product_line\", \"software_project\", \"software_requirements\", \"software_requirements\", \"software_solution\", \"software_solution\", \"solution_method\", \"solution_method\", \"stack_overflow\", \"standard\", \"standard\", \"star\", \"stem\", \"stem\", \"step_size\", \"stereo_matching\", \"stimuli\", \"stimuli\", \"stream_cipher\", \"strict_avalanche_criteria\", \"student_engagement\", \"students\", \"students'_ability\", \"students'_learning\", \"students'_performance\", \"students'_programming\", \"subject_descriptors\", \"subjects\", \"surface\", \"surface\", \"synthetic_aperture_radar\", \"synthetic_aperture_radar\", \"synthetic_aperture_radar\", \"synthetic_data\", \"synthetic_data\", \"system_calls\", \"system_development\", \"system_development\", \"system_performance\", \"system_performance\", \"system_performance\", \"system_performance\", \"system_software\", \"system_software\", \"systematic_literature\", \"systematic_literature\", \"systematic_literature_review\", \"systematic_literature_review\", \"tablet\", \"tablet\", \"tablet\", \"target_image\", \"task_execution\", \"task_scheduling\", \"teach\", \"teach\", \"teaching\", \"teaching_process\", \"team\", \"team\", \"team\", \"teamwork\", \"thinking\", \"thinking\", \"time_delay\", \"time_step\", \"tracking_algorithm\", \"tracking_algorithm\", \"tracking_error\", \"traditional_techniques\", \"transmit_power\", \"tumor\", \"tutoring_system\", \"uncertain_data\", \"velocity_field\", \"verifier\", \"vibration\", \"virtual_screening\", \"visual_cryptography\", \"visual_features\", \"visual_features\", \"visual_features\", \"visual_information\", \"visual_object\", \"visual_programming\", \"visual_tracking\", \"wall\", \"week\", \"workstation\", \"worm\", \"x-ray\", \"x-ray\", \"xeon_phi\", \"xilinx\", \"xml_documents\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 10, 6, 7, 9, 3, 4, 8, 5, 2]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el1272021583943260321899407939\", ldavis_el1272021583943260321899407939_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el1272021583943260321899407939\", ldavis_el1272021583943260321899407939_data);\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el1272021583943260321899407939\", ldavis_el1272021583943260321899407939_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 845,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "model_viz = pyLDAvis.prepare(beta,normalized_theta,docs_length,vocab,word_counts)\n",
    "pyLDAvis.display(model_viz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot Of Dominant Topics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 846,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic 1</th>\n",
       "      <th>Topic 2</th>\n",
       "      <th>Topic 3</th>\n",
       "      <th>Topic 4</th>\n",
       "      <th>Topic 5</th>\n",
       "      <th>Topic 6</th>\n",
       "      <th>Topic 7</th>\n",
       "      <th>Topic 8</th>\n",
       "      <th>Topic 9</th>\n",
       "      <th>Topic 10</th>\n",
       "      <th>dominant_topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001155</td>\n",
       "      <td>0.001578</td>\n",
       "      <td>0.989054</td>\n",
       "      <td>0.001182</td>\n",
       "      <td>0.001116</td>\n",
       "      <td>0.001067</td>\n",
       "      <td>0.001083</td>\n",
       "      <td>0.001381</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>0.499577</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.500235</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003744</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.002603</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.001054</td>\n",
       "      <td>0.000321</td>\n",
       "      <td>0.046038</td>\n",
       "      <td>0.511171</td>\n",
       "      <td>0.435012</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.992166</td>\n",
       "      <td>0.007487</td>\n",
       "      <td>0.000048</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.999829</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic 1   Topic 2   Topic 3   Topic 4   Topic 5   Topic 6   Topic 7  \\\n",
       "0  0.001155  0.001578  0.989054  0.001182  0.001116  0.001067  0.001083   \n",
       "1  0.000022  0.000030  0.499577  0.000023  0.000021  0.000020  0.000021   \n",
       "2  0.003744  0.000023  0.002603  0.000017  0.001054  0.000321  0.046038   \n",
       "3  0.000040  0.000055  0.000043  0.000041  0.000039  0.992166  0.007487   \n",
       "4  0.999829  0.000025  0.000019  0.000018  0.000017  0.000017  0.000017   \n",
       "\n",
       "    Topic 8   Topic 9  Topic 10  dominant_topic  \n",
       "0  0.001381  0.001246  0.001137               2  \n",
       "1  0.000026  0.000024  0.500235               9  \n",
       "2  0.511171  0.435012  0.000017               7  \n",
       "3  0.000048  0.000043  0.000039               5  \n",
       "4  0.000021  0.000019  0.000018               0  "
      ]
     },
     "execution_count": 846,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "df_document_topic= DataFrame(theta,columns=['Topic 1','Topic 2','Topic 3','Topic 4','Topic 5','Topic 6','Topic 7','Topic 8','Topic 9','Topic 10'])\n",
    "df_document_topic['dominant_topic'] = np.argmax(df_document_topic.values, axis=1)\n",
    "df_document_topic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 847,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic 1</th>\n",
       "      <th>Topic 2</th>\n",
       "      <th>Topic 3</th>\n",
       "      <th>Topic 4</th>\n",
       "      <th>Topic 5</th>\n",
       "      <th>Topic 6</th>\n",
       "      <th>Topic 7</th>\n",
       "      <th>Topic 8</th>\n",
       "      <th>Topic 9</th>\n",
       "      <th>Topic 10</th>\n",
       "      <th>dominant_topic</th>\n",
       "      <th>terms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.003693</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2</td>\n",
       "      <td>linear_discriminant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>9</td>\n",
       "      <td>accident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.001388</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2</td>\n",
       "      <td>healthy_subjects</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.002975</td>\n",
       "      <td>9</td>\n",
       "      <td>smoothness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.002310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2</td>\n",
       "      <td>subjects</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic 1   Topic 2   Topic 3   Topic 4   Topic 5   Topic 6   Topic 7  \\\n",
       "0  0.000004  0.000006  0.003693  0.000004  0.000004  0.000004  0.000004   \n",
       "1  0.000004  0.000006  0.000005  0.000004  0.000004  0.000004  0.000004   \n",
       "2  0.000004  0.000006  0.001388  0.000004  0.000004  0.000004  0.000004   \n",
       "3  0.000004  0.000006  0.000005  0.000004  0.000004  0.000004  0.000004   \n",
       "4  0.000004  0.000006  0.002310  0.000004  0.000004  0.000004  0.000004   \n",
       "\n",
       "    Topic 8   Topic 9  Topic 10  dominant_topic                terms  \n",
       "0  0.000005  0.000005  0.000004               2  linear_discriminant  \n",
       "1  0.000005  0.000005  0.001702               9             accident  \n",
       "2  0.000005  0.000005  0.000004               2     healthy_subjects  \n",
       "3  0.000005  0.000005  0.002975               9           smoothness  \n",
       "4  0.000005  0.000005  0.000004               2             subjects  "
      ]
     },
     "execution_count": 847,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "df_document_topic_term= DataFrame(beta.transpose(),columns=['Topic 1','Topic 2','Topic 3','Topic 4','Topic 5','Topic 6','Topic 7','Topic 8','Topic 9','Topic 10'])\n",
    "df_document_topic_term['dominant_topic'] = np.argmax(df_document_topic_term.values, axis=1)\n",
    "df_document_topic_term['terms']=vocab\n",
    "df_document_topic_term.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 848,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic 1</th>\n",
       "      <th>Topic 2</th>\n",
       "      <th>Topic 3</th>\n",
       "      <th>Topic 4</th>\n",
       "      <th>Topic 5</th>\n",
       "      <th>Topic 6</th>\n",
       "      <th>Topic 7</th>\n",
       "      <th>Topic 8</th>\n",
       "      <th>Topic 9</th>\n",
       "      <th>Topic 10</th>\n",
       "      <th>dominant_topic</th>\n",
       "      <th>terms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.003693</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2</td>\n",
       "      <td>linear_discriminant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>9</td>\n",
       "      <td>accident</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.001388</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2</td>\n",
       "      <td>healthy_subjects</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.002975</td>\n",
       "      <td>9</td>\n",
       "      <td>smoothness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.002310</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2</td>\n",
       "      <td>subjects</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000838</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000812</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.002331</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>8</td>\n",
       "      <td>assists</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>7</td>\n",
       "      <td>export</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.010146</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.008380</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>2</td>\n",
       "      <td>practitioner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.002161</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.002833</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>6</td>\n",
       "      <td>tags</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.001597</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>5</td>\n",
       "      <td>groundwater</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Topic 1   Topic 2   Topic 3   Topic 4   Topic 5   Topic 6   Topic 7  \\\n",
       "0  0.000004  0.000006  0.003693  0.000004  0.000004  0.000004  0.000004   \n",
       "1  0.000004  0.000006  0.000005  0.000004  0.000004  0.000004  0.000004   \n",
       "2  0.000004  0.000006  0.001388  0.000004  0.000004  0.000004  0.000004   \n",
       "3  0.000004  0.000006  0.000005  0.000004  0.000004  0.000004  0.000004   \n",
       "4  0.000004  0.000006  0.002310  0.000004  0.000004  0.000004  0.000004   \n",
       "5  0.000004  0.000006  0.000005  0.000004  0.000838  0.000004  0.000812   \n",
       "6  0.000004  0.000006  0.000005  0.000004  0.000004  0.000402  0.000004   \n",
       "7  0.000004  0.000006  0.010146  0.000004  0.000004  0.000004  0.000004   \n",
       "8  0.002161  0.000006  0.000005  0.000004  0.000004  0.000004  0.002833   \n",
       "9  0.000004  0.000006  0.000005  0.000004  0.000004  0.001597  0.000004   \n",
       "\n",
       "    Topic 8   Topic 9  Topic 10  dominant_topic                terms  \n",
       "0  0.000005  0.000005  0.000004               2  linear_discriminant  \n",
       "1  0.000005  0.000005  0.001702               9             accident  \n",
       "2  0.000005  0.000005  0.000004               2     healthy_subjects  \n",
       "3  0.000005  0.000005  0.002975               9           smoothness  \n",
       "4  0.000005  0.000005  0.000004               2             subjects  \n",
       "5  0.000005  0.002331  0.000004               8              assists  \n",
       "6  0.003099  0.000005  0.000004               7               export  \n",
       "7  0.001036  0.008380  0.000004               2         practitioner  \n",
       "8  0.001036  0.000005  0.000004               6                 tags  \n",
       "9  0.000005  0.000005  0.000004               5          groundwater  "
      ]
     },
     "execution_count": 848,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_document_topic_term.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 849,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic 1              0.003024\n",
      "Topic 2              0.000006\n",
      "Topic 3              0.000005\n",
      "Topic 4              0.000004\n",
      "Topic 5              0.000004\n",
      "Topic 6              0.000004\n",
      "Topic 7              0.000004\n",
      "Topic 8              0.000005\n",
      "Topic 9              0.000005\n",
      "Topic 10             0.000004\n",
      "dominant_topic              0\n",
      "terms             manufacture\n",
      "Name: 20, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df_document_topic_term.loc[20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 850,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\utils.py:551: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  np.asarray(values).astype(np.float)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f6ecaa8a30>"
      ]
     },
     "execution_count": 850,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "import seaborn as sns\n",
    "sns.countplot(df_document_topic.dominant_topic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gold Standard**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 851,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "gs=clean_corpus['gs']\n",
    "gs.pop('o')\n",
    "gs_termcounts=[]\n",
    "core_concepts=[]\n",
    "for core_concept in gs:\n",
    "    core_concepts.append(core_concept)\n",
    "    gs_termcounts.append(len(gs[core_concept]))\n",
    "plt.bar(core_concepts,gs_termcounts,width=0.8,color='purple')\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Topics Size\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 852,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "label=labeled_corpus['label']\n",
    "filtered_labels=[]\n",
    "for i in range(len(vocab)):\n",
    "    id,word=id2word[i]\n",
    "    index=terms_list.index(word)\n",
    "    filtered_labels.append(label[index])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Clusters Formation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 853,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters=[]\n",
    "for i in range(10):\n",
    "    cluster=[]\n",
    "    for j in range(len(vocab)):\n",
    "        row=df_document_topic_term.loc[j]\n",
    "        if(row['dominant_topic']==i):\n",
    "            cluster.append(row['terms'])\n",
    "    clusters.append(cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 854,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Terms    TF-IDF  term counts  Saliency\n",
      "0             linear_discriminant  0.016118           11 -0.000490\n",
      "1                        accident  0.092058            5 -0.000388\n",
      "2                healthy_subjects  1.058663            5 -0.000338\n",
      "3                      smoothness  3.493588           11 -0.000672\n",
      "4                        subjects  0.577453            5 -0.000507\n",
      "...                           ...       ...          ...       ...\n",
      "4518                         makd  2.540792            6 -0.000219\n",
      "4519                        simit  1.111596            7 -0.000160\n",
      "4520              memory_contents  0.317599            3 -0.000256\n",
      "4521  computer_network_technology  1.587995            5 -0.000377\n",
      "4522                  word_clouds  5.081583            8 -0.000130\n",
      "\n",
      "[4523 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "#determining measures of all terms\n",
    "terms_saliency={}\n",
    "for term in vocab:\n",
    "    index=word2id[term]\n",
    "    terms_saliency[term]=saliency[index]\n",
    "df_terms=pd.DataFrame(vocab,columns=['Terms'])\n",
    "df_terms['TF-IDF']=new_tfidf\n",
    "df_terms['term counts']=word_counts\n",
    "df_terms['Saliency']=terms_saliency.values()\n",
    "print(df_terms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Evaluation (With and Without Gold Standard)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 856,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_matrix=model.topic_word_\n",
    "features_matrix=features_matrix.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 857,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\pairwise.py:57: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dtype = np.float\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\pairwise.py:57: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dtype = np.float\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3989087384375431\n"
     ]
    }
   ],
   "source": [
    "#computing silhouette of samples composed of features (freq,tf-idf,saliency)\n",
    "samples=measures\n",
    "y_pred_1=df_document_topic_term.dominant_topic.tolist()\n",
    "from sklearn.metrics import silhouette_score\n",
    "silhouette=silhouette_score(features_matrix,y_pred_1)\n",
    "print(silhouette)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 858,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "262.8896074408194\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import calinski_harabasz_score\n",
    "#computing calinski of samples composed of features (freq,tf-idf,saliency)\n",
    "calinski_harabasz_score=calinski_harabasz_score(features_matrix,y_pred_1)\n",
    "print(calinski_harabasz_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 859,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4208\n",
      "digital_fabrication\n",
      "n\n",
      "4523\n"
     ]
    }
   ],
   "source": [
    "#NB: Here I found that there is an error in one of the true labels in GS  so I corrected it.\n",
    "for i in range(len(filtered_labels)):\n",
    "    label=filtered_labels[i]\n",
    "    if (label=='n'):\n",
    "        print(i)\n",
    "print(vocab[4208])\n",
    "print(filtered_labels[4208])\n",
    "print(len(filtered_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 860,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_labels[4208]='o'\n",
    "y_pred=df_document_topic_term['dominant_topic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 861,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 10   8   5   6   6  13   7  10  11  10   0]\n",
      " [ 15   4  17  16   2   7   9  15  22  15   0]\n",
      " [ 16  18  19  20  21   7  19  21  24  19   0]\n",
      " [ 17  11  16  16  12  19  13  16  22  16   0]\n",
      " [ 11   8  11   8   7  13  15  10  14  11   0]\n",
      " [  9   8   9  11  10   9   7   8  10  13   0]\n",
      " [  9   6   4   7   8   9   4   2  14   9   0]\n",
      " [ 11   7  10   9  10  10   7   9  15  16   0]\n",
      " [ 30  19  18  22  26  22  18  21  17  25   0]\n",
      " [  6   7   5   8   6   3   9   5   4  11   0]\n",
      " [397 281 342 363 288 340 299 331 308 364   0]]\n"
     ]
    }
   ],
   "source": [
    "labels_gs=[]\n",
    "dictionary_label={'operating_systems':0,'network_security':1,'machine_learning':2,'data_structures':3,'cryptography':4,'distributed_computing':5,'computer_programming':6,'software_engineering':7,'computer_graphics':8,'algorithm_design':9,'o':10}\n",
    "for label in filtered_labels:\n",
    "    labels_gs.append(dictionary_label[label])\n",
    "df_document_topic_term['gs label'] =labels_gs\n",
    "df_document_topic_term.head()\n",
    "from sklearn.metrics import confusion_matrix\n",
    "conf_matrix=confusion_matrix(labels_gs,df_document_topic_term['dominant_topic'])\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 862,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "macro: p 0.020911261273859207, r 0.08237298286358305, f1 0.032366766983287776\n",
      "micro: p 0.023435772717223084, r 0.023435772717223084, f1 0.02343577271722308\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, precision_recall_fscore_support\n",
    "p_macro, r_macro, f_macro, support_macro \\\n",
    "    = precision_recall_fscore_support(y_true=labels_gs, y_pred=y_pred, labels=[0,1,2,3,4,5,6,7,8,9,10], average='macro')\n",
    "\n",
    "p_micro, r_micro, f_micro, support_micro\\\n",
    "    = precision_recall_fscore_support(y_true=labels_gs, y_pred=y_pred, labels=[0,1,2,3,4,5,6,7,8,9,10], average='micro')\n",
    "\n",
    "print('macro: p {}, r {}, f1 {}'.format(p_macro, r_macro, f_macro))\n",
    "\n",
    "print('micro: p {}, r {}, f1 {}'.format(p_micro, r_micro, f_micro))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 863,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.1710204919421085e-05\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\cluster\\_supervised.py:127: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  dtype=np.int)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import adjusted_rand_score\n",
    "rand_score=adjusted_rand_score(labels_gs,df_document_topic_term['dominant_topic'])\n",
    "print(rand_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This part is to be updated and continued**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 864,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00010277422538775455\n"
     ]
    }
   ],
   "source": [
    "#computing pairwise precision\n",
    "no_of_pairs=[]\n",
    "for cluster in clusters:\n",
    "    length=len(cluster)\n",
    "    val=length*(length-1)/2\n",
    "    no_of_pairs.append(val)\n",
    "den=0\n",
    "for value in no_of_pairs:\n",
    "    den=den+value\n",
    "num=0\n",
    "for i in range(11):\n",
    "    num=num+conf_matrix[i][i]\n",
    "pairwise_precision=num/den\n",
    "print(pairwise_precision)"
   ]
  }
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