adding ability to run trinmfk without having to run nmfk first

Author: MaksimEkin

Diff for: TELF/factorization/TriNMFk.py

@@ -19,8 +19,8 @@
 import numpy as np
 import warnings
 import scipy.sparse
-import numpy as np
 import os
+from pathlib import Path
 
 try:
     import cupy as cp
@@ -87,6 +87,7 @@ class TriNMFk():
     def __init__(self,
                  experiment_name="TriNMFk",
                  nmfk_params={},
+                 save_path = "TriNMFk",
                  nmf_verbose=False,
                  use_gpu=False,
                  n_jobs=-1,
@@ -108,6 +109,8 @@ def __init__(self,
             Name used for the experiment. Default is "TriNMFk".
         nmfk_params : str, optional
             Parameters for NMFk. See documentation for NMFk for the options.
+        save_path : str, optional
+            Used for save location when NMFk fit is not performed first, and TriNMFk fit is done.
         nmf_verbose : bool, optional
             If True, shows progress in each NMF operation. The default is False.
         use_gpu : bool, optional
@@ -151,8 +154,9 @@ def __init__(self,
         self.nmfk_fit = False
         self.pruned = pruned
         self.transpose = transpose
-        self.save_path = "",
+        self.save_path = save_path
         self.verbose = verbose
+        self.save_path_full = ""
 
         # organize n_jobs
         n_jobs, self.use_gpu = organize_n_jobs(use_gpu, n_jobs)
@@ -206,7 +210,7 @@ def fit_nmfk(self, X, Ks, note=""):
 
         # Do NMFk
         nmfk_results = self.nmfk.fit(X, Ks, self.experiment_name, note)
-        self.save_path = os.path.join(self.nmfk.save_path, self.nmfk.experiment_name)
+        self.save_path_full = self.nmfk.save_path_full
 
         # Do nmfk here
         self.nmfk_fit = True
@@ -215,7 +219,8 @@ def fit_nmfk(self, X, Ks, note=""):
 
     def fit_tri_nmfk(self, X, k1k2:tuple):
         """
-        Factorize the input matrix ``X``, after applying ``fit_nmfk()`` to select the ``Wk`` and ``Hk``, to factorize the given matrix with ``k1k2=(Wk, Hk)``.
+        Factorize the input matrix ``X``.\n
+        after applying ``fit_nmfk()`` to select the ``Wk`` and ``Hk``, to factorize the given matrix with ``k1k2=(Wk, Hk)``.
 
         Parameters
         ----------
@@ -233,8 +238,20 @@ def fit_tri_nmfk(self, X, k1k2:tuple):
 
 
         if not self.nmfk_fit:
-            warnings.warn("NMFk needs to be fit first. Use fit_nmfk function!")
-            return
+            name = (
+                str(self.experiment_name)
+                + "_"
+                + str(self.n_iters)
+                + "iters_"
+                + str(self.n_inits)
+                + "inits"
+            )
+            self.save_path_full = os.path.join(self.save_path, name)
+            try:
+                if not Path(self.save_path_full).is_dir():
+                    Path(self.save_path_full).mkdir(parents=True)
+            except Exception as e:
+                print(e)
 
         if self.transpose:
             if isinstance(X, np.ndarray):
@@ -306,7 +323,7 @@ def fit_tri_nmfk(self, X, k1k2:tuple):
 
         # save the results
         np.savez_compressed(
-                    self.save_path
+                    self.save_path_full
                     + "/WSH"
                     + "_k="
                     + str(k1k2)

Diff for: docs/TELF.factorization.html

@@ -760,14 +760,15 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Link to this headi
 others to do so.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="TELF.factorization.TriNMFk.TriNMFk">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TELF.factorization.TriNMFk.</span></span><span class="sig-name descname"><span class="pre">TriNMFk</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiment_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'TriNMFk'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmfk_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmf_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_gpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_consensus_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0,</span> <span class="pre">0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_inits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pruned</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TELF/factorization/TriNMFk.html#TriNMFk"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TELF.factorization.TriNMFk.TriNMFk" title="Link to this definition">#</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">TELF.factorization.TriNMFk.</span></span><span class="sig-name descname"><span class="pre">TriNMFk</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">experiment_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'TriNMFk'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmfk_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">{}</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'TriNMFk'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nmf_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_gpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_consensus_stopping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0,</span> <span class="pre">0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_inits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pruned</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TELF/factorization/TriNMFk.html#TriNMFk"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TELF.factorization.TriNMFk.TriNMFk" title="Link to this definition">#</a></dt>
 <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
 <p>TriNMFk is a Non-negative Matrix Factorization module with the capability to do automatic model determination for both estimating the number of latent patterns (<code class="docutils literal notranslate"><span class="pre">Wk</span></code>) and clusters (<code class="docutils literal notranslate"><span class="pre">Hk</span></code>).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>experiment_name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name used for the experiment. Default is “TriNMFk”.</p></li>
 <li><p><strong>nmfk_params</strong> (<em>str</em><em>, </em><em>optional</em>) – Parameters for NMFk. See documentation for NMFk for the options.</p></li>
+<li><p><strong>save_path</strong> (<em>str</em><em>, </em><em>optional</em>) – Used for save location when NMFk fit is not performed first, and TriNMFk fit is done.</p></li>
 <li><p><strong>nmf_verbose</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, shows progress in each NMF operation. The default is False.</p></li>
 <li><p><strong>use_gpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – If True, uses GPU for operations. The default is True.</p></li>
 <li><p><strong>n_jobs</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of parallel jobs. Use -1 to use all available resources. The default is 1.</p></li>
@@ -819,7 +820,8 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Link to this headi
 <dl class="py method">
 <dt class="sig sig-object py" id="TELF.factorization.TriNMFk.TriNMFk.fit_tri_nmfk">
 <span class="sig-name descname"><span class="pre">fit_tri_nmfk</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">X</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k1k2</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">tuple</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/TELF/factorization/TriNMFk.html#TriNMFk.fit_tri_nmfk"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#TELF.factorization.TriNMFk.TriNMFk.fit_tri_nmfk" title="Link to this definition">#</a></dt>
-<dd><p>Factorize the input matrix <code class="docutils literal notranslate"><span class="pre">X</span></code>, after applying <code class="docutils literal notranslate"><span class="pre">fit_nmfk()</span></code> to select the <code class="docutils literal notranslate"><span class="pre">Wk</span></code> and <code class="docutils literal notranslate"><span class="pre">Hk</span></code>, to factorize the given matrix with <code class="docutils literal notranslate"><span class="pre">k1k2=(Wk,</span> <span class="pre">Hk)</span></code>.</p>
+<dd><p>Factorize the input matrix <code class="docutils literal notranslate"><span class="pre">X</span></code>.</p>
+<p>after applying <code class="docutils literal notranslate"><span class="pre">fit_nmfk()</span></code> to select the <code class="docutils literal notranslate"><span class="pre">Wk</span></code> and <code class="docutils literal notranslate"><span class="pre">Hk</span></code>, to factorize the given matrix with <code class="docutils literal notranslate"><span class="pre">k1k2=(Wk,</span> <span class="pre">Hk)</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">