- [important_notes_StandardScaler](https://scikit-learn.org/stable/au…

src/rasbt_ml_book_code/rasbt_ch03.py

@@ -1,11 +1,32 @@
 ## SOURCE -- https://github.com/rasbt/machine-learning-book/blob/main/ch03/ch03.py
+# conda activate env2_det2
+"""
+- [important_notes_StandardScaler](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section)
+- more importantly, they can degrade the predictive performance of many machine learning algorithms. Unscaled data can also slow down or even prevent the convergence of many gradient-based estimators.
+- more importantly that all features vary on comparable scales. 
+- 
+https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
+https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section
+
+https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler.transform
+transform(X[, copy])
+Perform standardization by centering and scaling.
+"""
+#
+"""
+-[sklearn.utils._bunch.Bunch](https://scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html#sklearn.utils.Bunch.keys)
+# Load Iris from - sklearn -- as a <class 'sklearn.utils._bunch.Bunch'>
+# https://scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html#sklearn.utils.Bunch.keys
+"""
+
 
 from sklearn import datasets
 import numpy as np
 from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import StandardScaler
+
 
-# Load Iris from - sklearn -- as a <class 'sklearn.utils._bunch.Bunch'>
-# https://scikit-learn.org/stable/modules/generated/sklearn.utils.Bunch.html#sklearn.utils.Bunch.keys
 
 iris = datasets.load_iris()
 
@@ -21,7 +42,28 @@
 print("--[INFO]--iris.frame--",iris.frame) # None
 print("--[INFO]--iris.data_module--",iris.data_module) # sklearn.datasets.data
 print('Class labels:', np.unique(y))
+#
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
+#
+print('Labels counts in y:', np.bincount(y)) ##weights=w
+print('Labels counts in y_train:', np.bincount(y_train))
+print('Labels counts in y_test:', np.bincount(y_test))
+#
+_ = plt.hist(y_train, bins='auto')
+plt.show()
+#
+# Standardizing the features:
+#
+sc = StandardScaler()
+sc.fit(X_train)
+X_train_std = sc.transform(X_train)
+X_test_std = sc.transform(X_test)
+
+
+# PDF == Probability density Function --->>  https://www.khanacademy.org/math/statistics-probability/random-variables-stats-library/random-variables-continuous/v/probability-density-functions
+# PDF == Probability density Function --->> https://en.wikipedia.org/wiki/Probability_density_function
+# that is, it is given by the area under the density function but above the horizontal axis and between the lowest and greatest values of the range.
+
 
 
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)
 

src/rasbt_ml_book_code/scikitLearn_std_scalar_1.py

@@ -0,0 +1,168 @@
+
+#ORIGINAL SOURCE - https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#plot-all-scaling-standard-scaler-section
+
+# Author:  Raghav RV <rvraghav93@gmail.com>
+#          Guillaume Lemaitre <g.lemaitre58@gmail.com>
+#          Thomas Unterthiner
+# License: BSD 3 clause
+
+import matplotlib as mpl
+import numpy as np
+from matplotlib import cm
+from matplotlib import pyplot as plt
+
+from sklearn.datasets import fetch_california_housing
+from sklearn.preprocessing import (
+    MaxAbsScaler,
+    MinMaxScaler,
+    Normalizer,
+    PowerTransformer,
+    QuantileTransformer,
+    RobustScaler,
+    StandardScaler,
+    minmax_scale,
+)
+
+df_calif_h = fetch_california_housing()
+
+print("[INFO]--type(df_calif_h)--",type(df_calif_h)) #<class 'sklearn.utils._bunch.Bunch'>
+print("[INFO]--df_calif_h.keys()--",df_calif_h.keys()) #dict_keys(['data', 'target', 'frame', 'target_names', 'feature_names', 'DESCR'])
+print("[INFO]--df_calif_h.keys()--",df_calif_h.frame) # None
+
+X_full, y_full = df_calif_h.data, df_calif_h.target
+feature_names = df_calif_h.feature_names
+print("[INFO]--df_calif---feature_names--",feature_names) # - ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude']
+
+# feature_mapping = {
+#     "MedInc": "Median income in block",
+#     "HouseAge": "Median house age in block",
+#     "AveRooms": "Average number of rooms",
+#     "AveBedrms": "Average number of bedrooms",
+#     "Population": "Block population",
+#     "AveOccup": "Average house occupancy",
+#     "Latitude": "House block latitude",
+#     "Longitude": "House block longitude",
+# }
+
+# # Take only 2 features to make visualization easier
+# # Feature MedInc has a long tail distribution.
+# # Feature AveOccup has a few but very large outliers.
+
+# features = ["MedInc", "AveOccup"]
+# features_idx = [feature_names.index(feature) for feature in features]
+# X = X_full[:, features_idx]
+# distributions = [
+#     ("Unscaled data", X),
+#     ("Data after standard scaling", StandardScaler().fit_transform(X)),
+#     ("Data after min-max scaling", MinMaxScaler().fit_transform(X)),
+#     ("Data after max-abs scaling", MaxAbsScaler().fit_transform(X)),
+#     (
+#         "Data after robust scaling",
+#         RobustScaler(quantile_range=(25, 75)).fit_transform(X),
+#     ),
+#     (
+#         "Data after power transformation (Yeo-Johnson)",
+#         PowerTransformer(method="yeo-johnson").fit_transform(X),
+#     ),
+#     (
+#         "Data after power transformation (Box-Cox)",
+#         PowerTransformer(method="box-cox").fit_transform(X),
+#     ),
+#     (
+#         "Data after quantile transformation (uniform pdf)",
+#         QuantileTransformer(
+#             output_distribution="uniform", random_state=42
+#         ).fit_transform(X),
+#     ),
+#     (
+#         "Data after quantile transformation (gaussian pdf)",
+#         QuantileTransformer(
+#             output_distribution="normal", random_state=42
+#         ).fit_transform(X),
+#     ),
+#     ("Data after sample-wise L2 normalizing", Normalizer().fit_transform(X)),
+# ]
+
+# # scale the output between 0 and 1 for the colorbar
+# y = minmax_scale(y_full)
+
+# # plasma does not exist in matplotlib < 1.5
+# cmap = getattr(cm, "plasma_r", cm.hot_r)
+
+
+# def create_axes(title, figsize=(16, 6)):
+#     fig = plt.figure(figsize=figsize)
+#     fig.suptitle(title)
+
+#     # define the axis for the first plot
+#     left, width = 0.1, 0.22
+#     bottom, height = 0.1, 0.7
+#     bottom_h = height + 0.15
+#     left_h = left + width + 0.02
+
+#     rect_scatter = [left, bottom, width, height]
+#     rect_histx = [left, bottom_h, width, 0.1]
+#     rect_histy = [left_h, bottom, 0.05, height]
+
+#     ax_scatter = plt.axes(rect_scatter)
+#     ax_histx = plt.axes(rect_histx)
+#     ax_histy = plt.axes(rect_histy)
+
+#     # define the axis for the zoomed-in plot
+#     left = width + left + 0.2
+#     left_h = left + width + 0.02
+
+#     rect_scatter = [left, bottom, width, height]
+#     rect_histx = [left, bottom_h, width, 0.1]
+#     rect_histy = [left_h, bottom, 0.05, height]
+
+#     ax_scatter_zoom = plt.axes(rect_scatter)
+#     ax_histx_zoom = plt.axes(rect_histx)
+#     ax_histy_zoom = plt.axes(rect_histy)
+
+#     # define the axis for the colorbar
+#     left, width = width + left + 0.13, 0.01
+
+#     rect_colorbar = [left, bottom, width, height]
+#     ax_colorbar = plt.axes(rect_colorbar)
+
+#     return (
+#         (ax_scatter, ax_histy, ax_histx),
+#         (ax_scatter_zoom, ax_histy_zoom, ax_histx_zoom),
+#         ax_colorbar,
+#     )
+
+
+# def plot_distribution(axes, X, y, hist_nbins=50, title="", x0_label="", x1_label=""):
+#     ax, hist_X1, hist_X0 = axes
+
+#     ax.set_title(title)
+#     ax.set_xlabel(x0_label)
+#     ax.set_ylabel(x1_label)
+
+#     # The scatter plot
+#     colors = cmap(y)
+#     ax.scatter(X[:, 0], X[:, 1], alpha=0.5, marker="o", s=5, lw=0, c=colors)
+
+#     # Removing the top and the right spine for aesthetics
+#     # make nice axis layout
+#     ax.spines["top"].set_visible(False)
+#     ax.spines["right"].set_visible(False)
+#     ax.get_xaxis().tick_bottom()
+#     ax.get_yaxis().tick_left()
+#     ax.spines["left"].set_position(("outward", 10))
+#     ax.spines["bottom"].set_position(("outward", 10))
+
+#     # Histogram for axis X1 (feature 5)
+#     hist_X1.set_ylim(ax.get_ylim())
+#     hist_X1.hist(
+#         X[:, 1], bins=hist_nbins, orientation="horizontal", color="grey", ec="grey"
+#     )
+#     hist_X1.axis("off")
+
+#     # Histogram for axis X0 (feature 0)
+#     hist_X0.set_xlim(ax.get_xlim())
+#     hist_X0.hist(
+#         X[:, 0], bins=hist_nbins, orientation="vertical", color="grey", ec="grey"
+#     )
+#     hist_X0.axis("off")