remoes telemetry

CHANGELOG.md

@@ -2,6 +2,8 @@
 
 ## 0.12.2dev
 
+- [Feature] Removes telemetry
+
 ## 0.12.1 (2024-02-07)
 
 - [Feature] Adds `ploomber-extension` as dependency

doc/reference/community.md

@@ -4,6 +4,3 @@
 
 [See here](https://docs.ploomber.io/en/latest/community/coc.html)
 
-## Telemetry
-
-[See here](https://docs.ploomber.io/en/latest/community/user-stats.html)

src/sklearn_evaluation/plot/grid_search.py

@@ -7,7 +7,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from six import string_types
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 
 from sklearn_evaluation.plot.matplotlib.bar import BarShifter
 
@@ -50,7 +49,6 @@ def _validate_kind_input(kind, valid):
     validate.keys(valid, passed=kind, name="kind")
 
 
-@SKLearnEvaluationLogger.log(feature="plot")
 @modify_exceptions
 def grid_search(
     cv_results_, change, subset=None, kind="line", cmap=None, ax=None, sort=True

src/sklearn_evaluation/plot/ks_statistics.py

@@ -29,7 +29,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.preprocessing import LabelEncoder
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 from ploomber_core.exceptions import modify_exceptions
 from sklearn_evaluation.plot.style import apply_theme
 
@@ -141,7 +140,6 @@ def _binary_ks_curve(y_true, y_score):
 
 
 @apply_theme()
-@SKLearnEvaluationLogger.log(feature="plot")
 @modify_exceptions
 def ks_statistic(
     y_true,

src/sklearn_evaluation/plot/learning_curve.py

@@ -1,12 +1,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 from ploomber_core.exceptions import modify_exceptions
 from sklearn_evaluation.plot.style import apply_theme
 
 
 @apply_theme()
-@SKLearnEvaluationLogger.log(feature="plot")
 @modify_exceptions
 def learning_curve(train_scores, test_scores, train_sizes, ax=None):
     """Plot a learning curve

src/sklearn_evaluation/plot/metrics.py

@@ -1,12 +1,10 @@
 import numpy as np
 from sklearn_evaluation.metrics import compute_at_thresholds
 from sklearn_evaluation.plot.util import set_default_ax
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 from ploomber_core.exceptions import modify_exceptions
 
 
 @set_default_ax
-@SKLearnEvaluationLogger.log(feature="plot")
 @modify_exceptions
 def metrics_at_thresholds(fn, y_true, y_score, n_thresholds=10, start=0.0, ax=None):
     """Plot metrics at increasing thresholds"""

src/sklearn_evaluation/plot/pca.py

@@ -1,6 +1,5 @@
 from sklearn.decomposition import PCA
 from sklearn.preprocessing import StandardScaler
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 from ploomber_core.exceptions import modify_exceptions
 from sklearn_evaluation.plot.style import apply_theme, get_color_palette
 
@@ -38,7 +37,6 @@ def _validate_inputs(X, n_components, target_names, colors, ax):
 
 @apply_theme()
 @modify_exceptions
-@SKLearnEvaluationLogger.log(feature="plot")
 def pca(X, y=None, target_names=None, n_components=2, colors=None, ax=None):
     """
     Plot principle component analysis curve.

src/sklearn_evaluation/plot/precision_recall.py

@@ -4,7 +4,6 @@
 from sklearn.metrics import auc
 from sklearn.metrics import precision_recall_curve
 from sklearn.preprocessing import label_binarize
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 from sklearn_evaluation.util import is_column_vector, is_row_vector
 from sklearn_evaluation.plot.plot import AbstractComposedPlot, AbstractPlot
 from sklearn_evaluation import __version__
@@ -133,7 +132,6 @@ class PrecisionRecall(AbstractPlot):
     """
 
     @modify_exceptions
-    @SKLearnEvaluationLogger.log(feature="plot", action="precision-recall-init")
     def __init__(self, precision, recall, label=None):
         self.precision = precision
         self.recall = recall
@@ -204,7 +202,6 @@ def plot(self, ax=None):
     def _from_data(cls):
         pass
 
-    @SKLearnEvaluationLogger.log(feature="plot", action="precision-recall-add")
     def __add__(self, another):
         return PrecisionRecallAdd(
             precisions=[self.precision, another.precision],
@@ -327,7 +324,6 @@ def plot(self, ax=None):
 
 
 @modify_exceptions
-@SKLearnEvaluationLogger.log(feature="plot")
 def precision_recall(y_true, y_score, ax=None):
     """
     Plot precision-recall curve.

src/sklearn_evaluation/plot/regression.py

@@ -22,7 +22,7 @@
 import numpy as np
 import pandas as pd
 
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
+
 from sklearn.linear_model import LinearRegression
 from ploomber_core.exceptions import modify_exceptions
 from sklearn_evaluation.plot.style import apply_theme, get_color_palette
@@ -43,7 +43,6 @@ def _check_parameter_validity(y_true, y_pred):
 
 
 @apply_theme()
-@SKLearnEvaluationLogger.log(feature="plot")
 @modify_exceptions
 def residuals(y_true, y_pred, ax=None):
     """
@@ -89,7 +88,6 @@ def residuals(y_true, y_pred, ax=None):
 
 
 @apply_theme()
-@SKLearnEvaluationLogger.log(feature="plot")
 @modify_exceptions
 def prediction_error(y_true, y_pred, ax=None):
     """
@@ -157,7 +155,6 @@ def prediction_error(y_true, y_pred, ax=None):
 
 
 @apply_theme()
-@SKLearnEvaluationLogger.log(feature="plot")
 @modify_exceptions
 def cooks_distance(X, y, ax=None):
     """Plots cooks distance.

src/sklearn_evaluation/plot/roc.py

@@ -2,7 +2,6 @@
 import matplotlib.pyplot as plt
 from sklearn.metrics import roc_curve, auc
 from sklearn.preprocessing import label_binarize, LabelBinarizer
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 from sklearn_evaluation.util import (
     is_column_vector,
     is_row_vector,
@@ -350,7 +349,6 @@ class ROC(AbstractPlot):
     .. versionadded:: 0.8.4
     """
 
-    @SKLearnEvaluationLogger.log(feature="plot", action="roc-init")
     @modify_exceptions
     def __init__(self, fpr, tpr, label=None):
         if fpr is None or tpr is None:
@@ -389,7 +387,6 @@ def __init__(self, fpr, tpr, label=None):
         self.tpr = tpr
         self.label = label
 
-    @SKLearnEvaluationLogger.log(feature="plot", action="roc-add")
     def __add__(self, other):
         roc_add_result = ROCAdd(self, other)
         roc_add_result.plot()

src/sklearn_evaluation/plot/target_analysis.py

@@ -20,7 +20,6 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 from sklearn.utils.multiclass import unique_labels, type_of_target
 from ploomber_core.exceptions import modify_exceptions
 from sklearn_evaluation.plot.style import get_color_palette, apply_theme
@@ -42,7 +41,6 @@ def _validate_target(y):
 
 
 @apply_theme()
-@SKLearnEvaluationLogger.log(feature="plot")
 @modify_exceptions
 def target_analysis(y_train, y_test=None, labels=None, colors=None, ax=None):
     """Target analysis plot for visualising class imbalance.

src/sklearn_evaluation/plot/validation_curve.py

@@ -1,12 +1,10 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 from ploomber_core.exceptions import modify_exceptions
 from sklearn_evaluation.plot.style import apply_theme
 
 
 @apply_theme()
-@SKLearnEvaluationLogger.log(feature="plot")
 @modify_exceptions
 def validation_curve(
     train_scores, test_scores, param_range, param_name=None, semilogx=False, ax=None

src/sklearn_evaluation/report/model_evaluator.py

@@ -11,7 +11,6 @@
 from sklearn_evaluation.report import ModelHeuristics, ReportSection
 import time
 import numpy as np
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 
 
 COMMUNITY_LINK = "https://ploomber.io/community"
@@ -276,7 +275,6 @@ def check_array_balance(self, array) -> bool:
         return all(expected_range.in_range(w) for w in weights)
 
 
-@SKLearnEvaluationLogger.log(feature="report", action="evaluate_model")
 def evaluate_model(model, y_true, y_pred, X_test=None, y_score=None, report_title=None):
     """
     Evaluates a given model and generates an HTML report

src/sklearn_evaluation/report/models_comparer.py

@@ -1,7 +1,6 @@
 from sklearn_evaluation import plot
 from sklearn_evaluation.report.util import run_if_args_are_not_none, check_model
 from sklearn_evaluation.report import ModelHeuristics, ReportSection, ModelEvaluator
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 
 
 class ModelsComparer(ModelHeuristics):
@@ -217,7 +216,6 @@ def add_combined_pr(self, X_test, y_true):
         self._add_section_to_report(combined_pr_section)
 
 
-@SKLearnEvaluationLogger.log(feature="report", action="compare_models")
 def compare_models(model_a, model_b, X_test, y_true, report_title=None):
     """
     Compares two models and generates an HTML report

src/sklearn_evaluation/report/report.py

@@ -1,6 +1,5 @@
 from jinja2 import Template
 from pathlib import Path
-from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
 
 try:
     import mistune
@@ -36,6 +35,5 @@ def __init__(self, evaluator, template=None):
     def _repr_html_(self):
         return self.rendered
 
-    @SKLearnEvaluationLogger.log(feature="report", action="save")
     def save(self, path):
         Path(path).write_text(self.rendered)