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Oct 1, 2023
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improper plots #122

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8 changes: 5 additions & 3 deletions src/rasbt_ml_book_code/rasbt_ch03.py
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Expand Up @@ -5,12 +5,14 @@
- 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
- 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
- https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler.transform
```
transform(X[, copy])
Perform standardization by centering and scaling.
```
"""
#
"""
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341 changes: 213 additions & 128 deletions src/rasbt_ml_book_code/scikitLearn_std_scalar_1.py
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Expand Up @@ -30,139 +30,224 @@
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",
# }
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)
features = ["MedInc", "AveOccup"]
features_idx = [feature_names.index(feature) for feature in features]
print("--features_idx--",features_idx)
print("--features_idx----X_full.shape-",X_full.shape)
X = X_full[:, features_idx]

print("--features_idx---type(X)-",type(X))
print("--features_idx---X.shape-",X.shape) #---X.shape- (20640, 2)


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")
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)
#plt.show() # OK Legends Only

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=""):

"""
"""
print("[INFO]---plot_distribution-axes---",axes)
print("[INFO]---plot_distribution-y---",y)

ax, hist_X1, hist_X0 = axes

ax.set_title(title)
print("[INFO]---plot_distribution----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")

print("---plot_distribution---type(hist_X1---",type(hist_X1))

# 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")
print("---plot_distribution---type(hist_X0---",type(hist_X0))
## <class 'matplotlib.axes._axes.Axes'>
#plt.show() # OK_1


def make_plot(item_idx):
"""
"""
print("--make_plot-making---")
title, X = distributions[item_idx]
print("--make_plot-----title-",title)

ax_zoom_out, ax_zoom_in, ax_colorbar = create_axes(title)
axarr = (ax_zoom_out, ax_zoom_in)
plot_distribution(
axarr[0],
X,
y,
hist_nbins=200,
x0_label=feature_mapping[features[0]],
x1_label=feature_mapping[features[1]],
title="Full data",
)

# zoom-in
zoom_in_percentile_range = (0, 99)
cutoffs_X0 = np.percentile(X[:, 0], zoom_in_percentile_range)
cutoffs_X1 = np.percentile(X[:, 1], zoom_in_percentile_range)

non_outliers_mask = np.all(X > [cutoffs_X0[0], cutoffs_X1[0]], axis=1) & np.all(
X < [cutoffs_X0[1], cutoffs_X1[1]], axis=1
)
print("--make_plot-----non_outliers_mask-",type(non_outliers_mask))
print("--make_plot-----non_outliers_mask.shape---",non_outliers_mask.shape)

plot_distribution(
axarr[1],
X[non_outliers_mask],
y[non_outliers_mask],
hist_nbins=50,
x0_label=feature_mapping[features[0]],
x1_label=feature_mapping[features[1]],
title="Zoom-in",
)

norm = mpl.colors.Normalize(y_full.min(), y_full.max())
mpl.colorbar.ColorbarBase(
ax_colorbar,
cmap=cmap,
norm=norm,
orientation="vertical",
label="Color mapping for values of y",
)


if __name__ == "__main__":
#make_plot(0)
#plt.show() #OK_1
make_plot(1)
plt.show() # OK_2
# make_plot(2)
# plt.show() # OK_2