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solved issue #44
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@parrt @matheusccouto @tjpell @feribg @escherba @GilesStrong @marcotama @Sharpen6 @RohanBhandari
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"""
A simple library of functions that provide feature importances
for scikit-learn random forest regressors and classifiers.
MIT License
Terence Parr, http://parrt.cs.usfca.edu
Kerem Turgutlu, https://www.linkedin.com/in/kerem-turgutlu-12906b65
"""
from distutils.version import LooseVersion
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
if LooseVersion(sklearn.__version__) >= LooseVersion("0.24"):
# In sklearn version 0.24, forest module changed to be private.
from sklearn.ensemble._forest import _generate_unsampled_indices
from sklearn.ensemble import _forest as forest
else:
# Before sklearn version 0.24, forest was public, supporting this.
from sklearn.ensemble.forest import _generate_unsampled_indices
from sklearn.ensemble import forest
from sklearn.model_selection import cross_val_score
from sklearn.base import clone
from sklearn.metrics import r2_score
from sklearn.metrics import f1_score
from sklearn.preprocessing import LabelEncoder
from scipy import stats
from pandas.api.types import is_numeric_dtype
from matplotlib.colors import ListedColormap
from matplotlib.ticker import FormatStrFormatter
from copy import copy
import warnings
import tempfile
from os import getpid, makedirs
from mpl_toolkits.axes_grid1 import make_axes_locatable
GREY = '#444443'
class PimpViz:
"""
For use with jupyter notebooks, plot_importances returns an instance
of this class so we display SVG not PNG.
"""
def __init__(self):
tmp = tempfile.gettempdir()
self.svgfilename = tmp+"/PimpViz_"+str(getpid())+".svg"
plt.tight_layout()
plt.savefig(self.svgfilename, bbox_inches='tight', pad_inches=0)
def _repr_svg_(self):
with open(self.svgfilename, "r", encoding='UTF-8') as f:
svg = f.read()
plt.close()
return svg
def save(self, filename):
plt.savefig(filename, bbox_inches='tight', pad_inches=0)
def view(self):
plt.show()
def close(self):
plt.close()
def importances(model, X_valid, y_valid, features=None, n_samples=5000, sort=True, metric=None, sample_weights = None):
"""
Compute permutation feature importances for scikit-learn models using
a validation set.
Given a Classifier or Regressor in model
and validation X and y data, return a data frame with columns
Feature and Importance sorted in reverse order by importance.
The validation data is needed to compute model performance
measures (accuracy or R^2). The model is not retrained.
You can pass in a list with a subset of features interesting to you.
All unmentioned features will be grouped together into a single meta-feature
on the graph. You can also pass in a list that has sublists like:
[['latitude', 'longitude'], 'price', 'bedrooms']. Each string or sublist
will be permuted together as a feature or meta-feature; the drop in
overall accuracy of the model is the relative importance.
The model.score() method is called to measure accuracy drops.
This version that computes accuracy drops with the validation set
is much faster than the OOB, cross validation, or drop column
versions. The OOB version is a less vectorized because it needs to dig
into the trees to get out of examples. The cross validation and drop column
versions need to do retraining and are necessarily much slower.
This function used OOB not validation sets in 1.0.5; switched to faster
test set version for 1.0.6. (breaking API change)
:param model: The scikit model fit to training data
:param X_valid: Data frame with feature vectors of the validation set
:param y_valid: Series with target variable of validation set
:param features: The list of features to show in importance graph.
These can be strings (column names) or lists of column
names. E.g., features = ['bathrooms', ['latitude', 'longitude']].
Feature groups can overlap, with features appearing in multiple.
:param n_samples: How many records of the validation set to use
to compute permutation importance. The default is
5000, which we arrived at by experiment over a few data sets.
As we cannot be sure how all data sets will react,
you can pass in whatever sample size you want. Pass in -1
to mean entire validation set. Our experiments show that
not too many records are needed to get an accurate picture of
feature importance.
:param sort: Whether to sort the resulting importances
:param metric: Metric in the form of callable(model, X_valid, y_valid, sample_weights) to evaluate for,
if not set default's to model.score()
:param sample_weights: set if a different weighting is required for the validation samples
return: A data frame with Feature, Importance columns
SAMPLE CODE
rf = RandomForestRegressor(n_estimators=100, n_jobs=-1)
X_train, y_train = ..., ...
X_valid, y_valid = ..., ...
rf.fit(X_train, y_train)
imp = importances(rf, X_valid, y_valid)
"""
def flatten(features):
all_features = set()
for sublist in features:
if isinstance(sublist, str):
all_features.add(sublist)
else:
for item in sublist:
all_features.add(item)
return all_features
if features is None:
# each feature in its own group
features = X_valid.columns.values
else:
req_feature_set = flatten(features)
model_feature_set = set(X_valid.columns.values)
# any features left over?
other_feature_set = model_feature_set.difference(req_feature_set)
if len(other_feature_set) > 0:
# if leftovers, we need group together as single new feature
features.append(list(other_feature_set))
X_valid, y_valid, sample_weights = sample(X_valid, y_valid, n_samples, sample_weights=sample_weights)
X_valid = X_valid.copy(deep=False) # we're modifying columns
if callable(metric):
baseline = metric(model, X_valid, y_valid, sample_weights)
else:
baseline = model.score(X_valid, y_valid, sample_weights)
imp = []
for group in features:
if isinstance(group, str):
save = X_valid[group].copy()
X_valid[group] = np.random.permutation(X_valid[group])
if callable(metric):
m = metric(model, X_valid, y_valid, sample_weights)
else:
m = model.score(X_valid, y_valid, sample_weights)
X_valid[group] = save
else:
save = {}
for col in group:
save[col] = X_valid[col].copy()
for col in group:
X_valid[col] = np.random.permutation(X_valid[col])
if callable(metric):
m = metric(model, X_valid, y_valid, sample_weights)
else:
m = model.score(X_valid, y_valid, sample_weights)
for col in group:
X_valid[col] = save[col]
imp.append(baseline - m)
# Convert and groups/lists into string column names
labels = []
for col in features:
if isinstance(col, list):
labels.append('\n'.join(col))
else:
labels.append(col)
I = pd.DataFrame(data={'Feature': labels, 'Importance': np.array(imp)})
I = I.set_index('Feature')
if sort:
I = I.sort_values('Importance', ascending=False)
return I
def sample(X_valid, y_valid, n_samples, sample_weights=None):
if n_samples < 0: n_samples = len(X_valid)
n_samples = min(n_samples, len(X_valid))
if n_samples < len(X_valid):
ix = np.random.choice(len(X_valid), n_samples)
X_valid = X_valid.iloc[ix].copy(deep=False) # shallow copy
y_valid = y_valid.iloc[ix].copy(deep=False)
if sample_weights is not None: sample_weights = sample_weights.iloc[ix].copy(deep=False)
return X_valid, y_valid, sample_weights
def sample_rows(X, n_samples):
if n_samples < 0: n_samples = len(X)
n_samples = min(n_samples, len(X))
if n_samples < len(X):
ix = np.random.choice(len(X), n_samples)
X = X.iloc[ix].copy(deep=False) # shallow copy
return X
def oob_importances(rf, X_train, y_train, n_samples=5000):
"""
Compute permutation feature importances for scikit-learn
RandomForestClassifier or RandomForestRegressor in arg rf.
Given training X and y data, return a data frame with columns
Feature and Importance sorted in reverse order by importance.
The training data is needed to compute out of bag (OOB)
model performance measures (accuracy or R^2). The model
is not retrained.
By default, sample up to 5000 observations to compute feature importances.
return: A data frame with Feature, Importance columns
SAMPLE CODE
rf = RandomForestRegressor(n_estimators=100, n_jobs=-1, oob_score=True)
X_train, y_train = ..., ...
rf.fit(X_train, y_train)
imp = oob_importances(rf, X_train, y_train)
"""
if isinstance(rf, RandomForestClassifier):
return permutation_importances(rf, X_train, y_train, oob_classifier_accuracy, n_samples)
elif isinstance(rf, RandomForestRegressor):
return permutation_importances(rf, X_train, y_train, oob_regression_r2_score, n_samples)
return None
def cv_importances(model, X_train, y_train, k=3):
"""
Compute permutation feature importances for scikit-learn models using
k-fold cross-validation (default k=3).
Given a Classifier or Regressor in model
and training X and y data, return a data frame with columns
Feature and Importance sorted in reverse order by importance.
Cross-validation observations are taken from X_train, y_train.
The model.score() method is called to measure accuracy drops.
return: A data frame with Feature, Importance columns
SAMPLE CODE
rf = RandomForestRegressor(n_estimators=100, n_jobs=-1)
X_train, y_train = ..., ...
rf.fit(X_train, y_train)
imp = cv_importances(rf, X_train, y_train)
"""
def score(model):
cvscore = cross_val_score(
model, # which model to use
X_train, y_train, # what training data to split up
cv=k) # number of folds/chunks
return np.mean(cvscore)
X_train = X_train.copy(deep=False) # shallow copy
baseline = score(model)
imp = []
for col in X_train.columns:
save = X_train[col].copy()
X_train[col] = np.random.permutation(X_train[col])
m = score(model)
X_train[col] = save
imp.append(baseline - m)
I = pd.DataFrame(data={'Feature': X_train.columns, 'Importance': np.array(imp)})
I = I.set_index('Feature')
I = I.sort_values('Importance', ascending=False)
return I
def permutation_importances(rf, X_train, y_train, metric, n_samples=5000):
imp = permutation_importances_raw(rf, X_train, y_train, metric, n_samples)
I = pd.DataFrame(data={'Feature':X_train.columns, 'Importance':imp})
I = I.set_index('Feature')
I = I.sort_values('Importance', ascending=False)
return I
def dropcol_importances(model, X_train, y_train, X_valid=None, y_valid=None, metric=None, sample_weights=None):
"""
Compute drop-column feature importances for scikit-learn.
Given a classifier or regression in model
and training X and y data, return a data frame with columns
Feature and Importance sorted in reverse order by importance.
A clone of model is trained once to get the baseline score and then
again, once per feature to compute the drop in either the model's .score() output
or a custom metric callable in the form of metric(model, X_valid, y_valid).
In case of a custom metric the X_valid and y_valid parameters should be set.
return: A data frame with Feature, Importance columns
SAMPLE CODE
rf = RandomForestRegressor(n_estimators=100, n_jobs=-1)
X_train, y_train = ..., ...
rf.fit(X_train, y_train)
imp = dropcol_importances(rf, X_train, y_train)
"""
if X_valid is None: X_valid = X_train
if y_valid is None: y_valid = y_train
model_ = clone(model)
model_.random_state = 999
model_.fit(X_train, y_train)
if callable(metric):
baseline = metric(model_, X_valid, y_valid, sample_weights)
else:
baseline = model_.score(X_valid, y_valid, sample_weights)
imp = []
for col in X_train.columns:
model_ = clone(model)
model_.random_state = 999
model_.fit(X_train.drop(col,axis=1), y_train)
if callable(metric):
s = metric(model_, X_valid.drop(col,axis=1), y_valid, sample_weights)
else:
s = model_.score(X_valid.drop(col,axis=1), y_valid, sample_weights)
drop_in_score = baseline - s
imp.append(drop_in_score)
imp = np.array(imp)
I = pd.DataFrame(data={'Feature':X_train.columns, 'Importance':imp})
I = I.set_index('Feature')
I = I.sort_values('Importance', ascending=False)
return I
def oob_dropcol_importances(rf, X_train, y_train):
"""
Compute drop-column feature importances for scikit-learn.
Given a RandomForestClassifier or RandomForestRegressor in rf
and training X and y data, return a data frame with columns
Feature and Importance sorted in reverse order by importance.
A clone of rf is trained once to get the baseline score and then
again, once per feature to compute the drop in out of bag (OOB)
score.
return: A data frame with Feature, Importance columns
SAMPLE CODE
rf = RandomForestRegressor(n_estimators=100, n_jobs=-1, oob_score=True)
X_train, y_train = ..., ...
rf.fit(X_train, y_train)
imp = oob_dropcol_importances(rf, X_train, y_train)
"""
rf_ = clone(rf)
rf_.random_state = 999
rf_.oob_score = True
rf_.fit(X_train, y_train)
baseline = rf_.oob_score_
imp = []
for col in X_train.columns:
rf_ = clone(rf)
rf_.random_state = 999
rf_.oob_score = True
rf_.fit(X_train.drop(col, axis=1), y_train)
drop_in_score = baseline - rf_.oob_score_
imp.append(drop_in_score)
imp = np.array(imp)
I = pd.DataFrame(data={'Feature':X_train.columns, 'Importance':imp})
I = I.set_index('Feature')
I = I.sort_values('Importance', ascending=False)
return I
def importances_raw(rf, X_train, y_train, n_samples=5000):
if isinstance(rf, RandomForestClassifier):
return permutation_importances_raw(rf, X_train, y_train, oob_classifier_accuracy, n_samples)
elif isinstance(rf, RandomForestRegressor):
return permutation_importances_raw(rf, X_train, y_train, oob_regression_r2_score, n_samples)
return None
def permutation_importances_raw(rf, X_train, y_train, metric, n_samples=5000):
"""
Return array of importances from pre-fit rf; metric is function
that measures accuracy or R^2 or similar. This function
works for regressors and classifiers.
"""
X_sample, y_sample, _ = sample(X_train, y_train, n_samples)
if not hasattr(rf, 'estimators_'):
rf.fit(X_sample, y_sample)
baseline = metric(rf, X_sample, y_sample)
X_train = X_sample.copy(deep=False) # shallow copy
y_train = y_sample
imp = []
for col in X_train.columns:
save = X_train[col].copy()
X_train[col] = np.random.permutation(X_train[col])
m = metric(rf, X_train, y_train)
X_train[col] = save
drop_in_metric = baseline - m
imp.append(drop_in_metric)
return np.array(imp)
def _get_unsampled_indices(tree, n_samples):
"""
An interface to get unsampled indices regardless of sklearn version.
"""
if LooseVersion(sklearn.__version__) >= LooseVersion("0.22"):
# Version 0.22 or newer uses 3 arguments.
from sklearn.ensemble.forest import _get_n_samples_bootstrap
n_samples_bootstrap = _get_n_samples_bootstrap(n_samples, n_samples)
return _generate_unsampled_indices(tree.random_state, n_samples, n_samples_bootstrap)
else:
# Version 0.21 or older uses only two arguments.
return _generate_unsampled_indices(tree.random_state, n_samples)
def oob_classifier_accuracy(rf, X_train, y_train):
"""
Compute out-of-bag (OOB) accuracy for a scikit-learn random forest
classifier. We learned the guts of scikit's RF from the BSD licensed
code:
https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/forest.py#L425
"""
X = X_train.values
y = y_train.values
n_samples = len(X)
n_classes = len(np.unique(y))
predictions = np.zeros((n_samples, n_classes))
for tree in rf.estimators_:
unsampled_indices = _get_unsampled_indices(tree, n_samples)
tree_preds = tree.predict_proba(X[unsampled_indices, :])
predictions[unsampled_indices] += tree_preds
predicted_class_indexes = np.argmax(predictions, axis=1)
predicted_classes = [rf.classes_[i] for i in predicted_class_indexes]
oob_score = np.mean(y == predicted_classes)
return oob_score
def oob_classifier_f1_score(rf, X_train, y_train):
"""
Compute out-of-bag (OOB) f1 score for a scikit-learn random forest
classifier. We learned the guts of scikit's RF from the BSD licensed
code:
https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/forest.py#L425
"""
X = X_train.values
y = y_train.values
n_samples = len(X)
n_classes = len(np.unique(y))
predictions = np.zeros((n_samples, n_classes))
for tree in rf.estimators_:
unsampled_indices = _get_unsampled_indices(tree, n_samples)
tree_preds = tree.predict_proba(X[unsampled_indices, :])
predictions[unsampled_indices] += tree_preds
predicted_class_indexes = np.argmax(predictions, axis=1)
predicted_classes = [rf.classes_[i] for i in predicted_class_indexes]
oob_score = f1_score(y, predicted_classes, average='macro')
return oob_score
def oob_regression_r2_score(rf, X_train, y_train):
"""
Compute out-of-bag (OOB) R^2 for a scikit-learn random forest
regressor. We learned the guts of scikit's RF from the BSD licensed
code:
https://github.com/scikit-learn/scikit-learn/blob/a24c8b46/sklearn/ensemble/forest.py#L702
"""
X = X_train.values if isinstance(X_train, pd.DataFrame) else X_train
y = y_train.values if isinstance(y_train, pd.Series) else y_train
n_samples = len(X)
predictions = np.zeros(n_samples)
n_predictions = np.zeros(n_samples)
for tree in rf.estimators_:
unsampled_indices = _get_unsampled_indices(tree, n_samples)
tree_preds = tree.predict(X[unsampled_indices, :])
predictions[unsampled_indices] += tree_preds
n_predictions[unsampled_indices] += 1
if (n_predictions == 0).any():
warnings.warn("Too few trees; some variables do not have OOB scores.")
n_predictions[n_predictions == 0] = 1
predictions /= n_predictions
oob_score = r2_score(y, predictions)
return oob_score
def stemplot_importances(df_importances,
yrot=0,
label_fontsize=10,
width=4,
minheight=1.5,
vscale=1.0,
imp_range=(-.002, .15),
color='#375FA5',
bgcolor=None, # seaborn uses '#F1F8FE'
xtick_precision=2,
title=None):
GREY = '#444443'
I = df_importances
unit = 1
imp = I.Importance.values
mindrop = np.min(imp)
maxdrop = np.max(imp)
imp_padding = 0.002
imp_range = (min(imp_range[0], mindrop - imp_padding), max(imp_range[1], maxdrop))
barcounts = np.array([f.count('\n')+1 for f in I.index])
N = np.sum(barcounts)
ymax = N * unit
# print(f"barcounts {barcounts}, N={N}, ymax={ymax}")
height = max(minheight, ymax * .27 * vscale)
plt.close()
fig = plt.figure(figsize=(width,height))
ax = plt.gca()
ax.set_xlim(*imp_range)
ax.set_ylim(0,ymax)
ax.spines['top'].set_linewidth(.3)
ax.spines['right'].set_linewidth(.3)
ax.spines['left'].set_linewidth(.3)
ax.spines['bottom'].set_linewidth(.3)
if bgcolor:
ax.set_facecolor(bgcolor)
yloc = []
y = barcounts[0]*unit / 2
yloc.append(y)
for i in range(1,len(barcounts)):
wprev = barcounts[i-1]
w = barcounts[i]
y += (wprev + w)/2 * unit
yloc.append(y)
yloc = np.array(yloc)
ax.xaxis.set_major_formatter(FormatStrFormatter(f'%.{xtick_precision}f'))
ax.set_xticks([maxdrop, imp_range[1]])
ax.tick_params(labelsize=label_fontsize, labelcolor=GREY)
ax.invert_yaxis() # labels read top-to-bottom
if title:
ax.set_title(title, fontsize=label_fontsize+1, fontname="Arial", color=GREY)
plt.hlines(y=yloc, xmin=imp_range[0], xmax=imp, lw=barcounts*1.2, color=color)
for i in range(len(I.index)):
plt.plot(imp[i], yloc[i], "o", color=color, markersize=barcounts[i]+2)
ax.set_yticks(yloc)
ax.set_yticklabels(I.index, fontdict={'verticalalignment': 'center'})
plt.tick_params(
pad=0,
axis='y',
which='both',
left=False)
# rotate y-ticks
if yrot is not None:
plt.yticks(rotation=yrot)
plt.tight_layout()
return PimpViz()
def plot_importances(df_importances,
yrot=0,
label_fontsize=10,
width=4,
minheight=1.5,
vscale=1,
imp_range=(-.002, .15),
color='#D9E6F5',
bgcolor=None, # seaborn uses '#F1F8FE'
xtick_precision=2,
title=None,
ax=None):
"""
Given an array or data frame of importances, plot a horizontal bar chart
showing the importance values.
:param df_importances: A data frame with Feature, Importance columns
:type df_importances: pd.DataFrame
:param width: Figure width in default units (inches I think). Height determined
by number of features.
:type width: int
:param minheight: Minimum plot height in default matplotlib units (inches?)
:type minheight: float
:param vscale: Scale vertical plot (default .25) to make it taller
:type vscale: float
:param label_fontsize: Font size for feature names and importance values
:type label_fontsize: int
:param yrot: Degrees to rotate feature (Y axis) labels
:type yrot: int
:param label_fontsize: The font size for the column names and x ticks
:type label_fontsize: int
:param scalefig: Scale width and height of image (widthscale,heightscale)
:type scalefig: 2-tuple of floats
:param xtick_precision: How many digits after decimal for importance values.
:type xtick_precision: int
:param xtick_precision: Title of plot; set to None to avoid.
:type xtick_precision: string
:param ax: Matplotlib "axis" to plot into
:return: None
SAMPLE CODE
rf = RandomForestRegressor(n_estimators=100, n_jobs=-1, oob_score=True)
X_train, y_train = ..., ...
rf.fit(X_train, y_train)
imp = importances(rf, X_test, y_test)
viz = plot_importances(imp)
viz.save('file.svg')
viz.save('file.pdf')
viz.view() # or just viz in notebook
"""
I = df_importances
unit = 1
ypadding = .1
imp = I.Importance.values
mindrop = np.min(imp)
maxdrop = np.max(imp)
imp_padding = 0.002
imp_range = (min(imp_range[0], mindrop - imp_padding), max(imp_range[1], maxdrop + imp_padding))
barcounts = np.array([f.count('\n')+1 for f in I.index])
N = np.sum(barcounts)
ymax = N * unit + len(I.index) * ypadding + ypadding
# print(f"barcounts {barcounts}, N={N}, ymax={ymax}")
height = max(minheight, ymax * .2 * vscale)
if ax is None:
plt.close()
fig, ax = plt.subplots(1,1,figsize=(width,height))
ax.set_xlim(*imp_range)
ax.set_ylim(0,ymax)
ax.spines['top'].set_linewidth(.3)
ax.spines['right'].set_linewidth(.3)
ax.spines['left'].set_linewidth(.3)
ax.spines['bottom'].set_linewidth(.3)
if bgcolor:
ax.set_facecolor(bgcolor)
yloc = []
y = barcounts[0]*unit / 2 + ypadding
yloc.append(y)
for i in range(1,len(barcounts)):
wprev = barcounts[i-1]
w = barcounts[i]
y += (wprev + w)/2 * unit + ypadding
yloc.append(y)
yloc = np.array(yloc)
ax.xaxis.set_major_formatter(FormatStrFormatter(f'%.{xtick_precision}f'))
# too close to show both max and right edge?
if maxdrop/imp_range[1] > 0.9 or maxdrop < 0.02:
ax.set_xticks([0, imp_range[1]])
else:
ax.set_xticks([0, maxdrop, imp_range[1]])
ax.tick_params(labelsize=label_fontsize, labelcolor=GREY)
ax.invert_yaxis() # labels read top-to-bottom
if title:
ax.set_title(title, fontsize=label_fontsize+1, fontname="Arial", color=GREY)
barcontainer = ax.barh(y=yloc, width=imp,
height=barcounts*unit,
tick_label=I.index,
color=color, align='center')
# Alter appearance of each bar
for rect in barcontainer.patches:
rect.set_linewidth(.5)
rect.set_edgecolor(GREY)
# rotate y-ticks
if yrot is not None:
ax.tick_params(labelrotation=yrot)
return PimpViz()
def oob_dependences(rf, X_train, n_samples=5000):
"""
Given a random forest model, rf, and training observation independent
variables in X_train (a dataframe), compute the OOB R^2 score using each var
as a dependent variable. We retrain rf for each var. Only numeric columns are considered.
By default, sample up to 5000 observations to compute feature dependencies.
:return: Return a DataFrame with Feature/Dependence values for each variable. Feature is the dataframe index.
"""
numcols = [col for col in X_train if is_numeric_dtype(X_train[col])]
X_train = sample_rows(X_train, n_samples)
df_dep = pd.DataFrame(columns=['Feature','Dependence'])
df_dep = df_dep.set_index('Feature')
for col in numcols:
X, y = X_train.drop(col, axis=1), X_train[col]
rf.fit(X, y)
df_dep.loc[col] = rf.oob_score_
df_dep = df_dep.sort_values('Dependence', ascending=False)
return df_dep
def feature_dependence_matrix(X_train,
rfrmodel=RandomForestRegressor(n_estimators=50, oob_score=True),
rfcmodel=RandomForestClassifier(n_estimators=50, oob_score=True),
cat_count=20,
zero=0.001,
sort_by_dependence=False,
n_samples=5000):
"""
Given training observation independent variables in X_train (a dataframe),
compute the feature importance using each var as a dependent variable using
a RandomForestRegressor or RandomForestClassifier. A RandomForestClassifer is
used when the number of the unique values for the dependent variable is less or
equal to the cat_count arg. We retrain a random forest for each var as target
using the others as independent vars. Only numeric columns are considered.
By default, sample up to 5000 observations to compute feature dependencies.
If feature importance is less than zero arg, force to 0. Force all negatives to 0.0.
Clip to 1.0 max. (Some importances could come back > 1.0 because removing that
feature sends R^2 very negative.)
:return: a non-symmetric data frame with the dependence matrix where each row is the importance of each var to the row's var used as a model target.
"""
numeric_cols = [col for col in X_train if is_numeric_dtype(X_train[col])]
cat_cols = [col for col in numeric_cols if X_train[col].value_counts().count() <= cat_count]
cat_cols_le = [col for col in cat_cols if X_train[col].dtypes == 'float' ]
for col in cat_cols_le:
le = LabelEncoder()
X_train[col] = le.fit_transform(X_train[col])
X_train = sample_rows(X_train, n_samples)
df_dep = pd.DataFrame(index=X_train.columns, columns=['Dependence']+X_train.columns.tolist())
for i,col in enumerate(numeric_cols):
X, y = X_train.drop(col, axis=1), X_train[col]
if col in cat_cols:
rf = clone(rfcmodel)
rf.fit(X,y)
imp = permutation_importances_raw(rf, X, y, oob_classifier_f1_score, n_samples)
else:
rf = clone(rfrmodel)
rf.fit(X,y)
imp = permutation_importances_raw(rf, X, y, oob_regression_r2_score, n_samples)
"""
Some RandomForestRegressor importances could come back > 1.0 because removing
that feature sends R^2 very negative. Clip them at 1.0. Also, features with
negative importance means that taking them out helps predict but we don't care
about that here. We want to know which features are collinear/predictive. Clip
at 0.0.
"""
imp = np.clip(imp, a_min=0.0, a_max=1.0)
imp[imp<zero] = 0.0
imp = np.insert(imp, i, 1.0)
df_dep.iloc[i] = np.insert(imp, 0, rf.oob_score_) # add overall dependence
if sort_by_dependence:
return df_dep.sort_values('Dependence', ascending=False)
return df_dep
def plot_dependence_heatmap(D,
color_threshold=0.6,
threshold=0.03,
cmap=None,
figsize=None,
value_fontsize=8,
label_fontsize=9,
precision=2,
xrot=70,
grid=True):
depdata = D.values.astype(float)
ncols, nrows = depdata.shape
if figsize:
fig = plt.figure(figsize=figsize)
colnames = list(D.columns.values)
colnames[0] = "$\\bf "+colnames[0]+"$" # bold Dependence word
plt.xticks(range(len(colnames)), colnames, rotation=xrot, horizontalalignment='right',
fontsize=label_fontsize, color=GREY)
plt.yticks(range(len(colnames[1:])), colnames[1:], verticalalignment='center',
fontsize=label_fontsize, color=GREY)
if cmap is None:
cw = plt.get_cmap('coolwarm')
cmap = ListedColormap([cw(x) for x in np.arange(color_threshold, .85, 0.01)])
elif isinstance(cmap, str):
cmap = plt.get_cmap(cmap)
cm = copy(cmap)
cm.set_under(color='white')
for x in range(ncols):
for y in range(nrows):
if (x+1) == y or depdata[x,y]<threshold:
depdata[x,y] = 0
if grid:
plt.grid(True, which='major', alpha=.25)
im = plt.imshow(depdata, cmap=cm, vmin=color_threshold, vmax=1.0, aspect='equal')
cb = plt.colorbar(im,
fraction=0.046,
pad=0.04,
ticks=[color_threshold,color_threshold+(1-color_threshold)/2,1.0])
cb.ax.tick_params(labelsize=label_fontsize, labelcolor=GREY, pad=0)
cb.outline.set_edgecolor('white')
plt.axvline(x=.5, lw=1, color=GREY)
for x in range(ncols):
for y in range(nrows):
if (x+1) == y:
plt.annotate('x', xy=(y, x),
horizontalalignment='center',
verticalalignment='center',
fontsize=value_fontsize, color=GREY)
if (x+1) != y and not np.isclose(round(depdata[x, y],precision), 0.0):
plt.annotate(myround(depdata[x, y], precision), xy=(y, x),
horizontalalignment='center',
verticalalignment='center',
fontsize=value_fontsize, color=GREY)
plt.tick_params(pad=0, axis='x', which='both')
ax = plt.gca()
ax.spines['top'].set_linewidth(.3)
ax.spines['right'].set_linewidth(.3)
ax.spines['left'].set_linewidth(1)
ax.spines['left'].set_edgecolor(GREY)
ax.spines['bottom'].set_linewidth(.3)
plt.tight_layout()
return PimpViz()
def get_feature_corr(df, method="spearman"):
if isinstance(df, pd.DataFrame):
result = df.corr(method=method).values
elif callable(method):
result = method(df)
elif method == "spearman":
result = stats.spearmanr(df).correlation
elif method == "pearson":
result = np.corrcoef(df)
else:
raise ValueError("unsupported correlation method")
return result
def feature_corr_matrix(df, method="spearman"):
"""
Return the Spearman's rank-order correlation (or another method) between all pairs
of features as a matrix with feature names as index and column names.
The diagonal will be all 1.0 as features are self correlated.
Spearman's correlation is the same thing as converting two variables
to rank values and then running a standard Pearson's correlation
on those ranked variables. Spearman's is nonparametric and does not
assume a linear relationship between the variables; it looks for
monotonic relationships.
:param df: dataframe containing features as columns, and without the target variable.
:param method: A string ("spearman", "pearson") or a callable function.
:return: a data frame with the correlation matrix
"""
corr = np.round(get_feature_corr(df, method=method), 4)
df_corr = pd.DataFrame(data=corr, index=df.columns, columns=df.columns)
return df_corr
def plot_corr_heatmap(df,
color_threshold=0.6,
cmap=None,
figsize=None,
value_fontsize=8,
label_fontsize=9,
precision=2,
xrot=80,
method="spearman"):
"""
Display the feature spearman's correlation matrix as a heatmap with
any abs(value)>color_threshold appearing with background color.
Spearman's correlation is the same thing as converting two variables
to rank values and then running a standard Pearson's correlation
on those ranked variables. Spearman's is nonparametric and does not
assume a linear relationship between the variables; it looks for
monotonic relationships.
SAMPLE CODE
from rfpimp import plot_corr_heatmap
viz = plot_corr_heatmap(df_train, save='/tmp/corrheatmap.svg',
figsize=(7,5), label_fontsize=13, value_fontsize=11)
viz.view() # or just viz in notebook
"""
corr = get_feature_corr(df, method=method)
if len(corr.shape) == 0:
corr = np.array([[1.0, corr],
[corr, 1.0]])
filtered = copy(corr)
filtered = np.abs(filtered) # work with abs but display negatives later
mask = np.ones_like(corr)
filtered[np.tril_indices_from(mask)] = -9999
if cmap is None:
cw = plt.get_cmap('coolwarm')
cmap = ListedColormap([cw(x) for x in np.arange(color_threshold, .85, 0.01)])
elif isinstance(cmap, str):
cmap = plt.get_cmap(cmap)
cm = copy(cmap)
cm.set_under(color='white')
if figsize:
plt.figure(figsize=figsize)
im = plt.imshow(filtered, cmap=cm, vmin=color_threshold, vmax=1, aspect='equal')
width, height = filtered.shape
for x in range(width):
for y in range(height):
if x == y:
plt.annotate('x', xy=(y, x),
horizontalalignment='center',
verticalalignment='center',
fontsize=value_fontsize, color=GREY)
if x < y:
plt.annotate(myround(corr[x, y], precision), xy=(y, x),
horizontalalignment='center',
verticalalignment='center',
fontsize=value_fontsize, color=GREY)
cb = plt.colorbar(im, fraction=0.046, pad=0.04, ticks=[color_threshold, color_threshold + (1 - color_threshold) / 2, 1.0])
cb.ax.tick_params(labelsize=label_fontsize, labelcolor=GREY, )
cb.outline.set_edgecolor('white')
plt.xticks(range(width), df.columns, rotation=xrot, horizontalalignment='right',
fontsize=label_fontsize, color=GREY)
plt.yticks(range(width), df.columns, verticalalignment='center',
fontsize=label_fontsize, color=GREY)
ax = plt.gca()
ax.spines['top'].set_linewidth(.3)
ax.spines['right'].set_linewidth(.3)
ax.spines['left'].set_linewidth(.3)
ax.spines['bottom'].set_linewidth(.3)
plt.tight_layout()
return PimpViz()
def rfnnodes(rf):
"""Return the total number of decision and leaf nodes in all trees of the forest."""
return sum(t.tree_.node_count for t in rf.estimators_)
def dectree_max_depth(tree):
"""
Return the max depth of this tree in terms of how many nodes; a single
root node gives height 1.
"""
children_left = tree.children_left
children_right = tree.children_right
def walk(node_id):
if (children_left[node_id] != children_right[node_id]): # decision node
left_max = 1 + walk(children_left[node_id])
right_max = 1 + walk(children_right[node_id])
return max(left_max, right_max)
else: # leaf
return 1
root_node_id = 0
return walk(root_node_id)
def rfmaxdepths(rf):
"""
Return the max depth of all trees in rf forest in terms of how many nodes
(a single root node for a single tree gives height 1)
"""
return [dectree_max_depth(t.tree_) for t in rf.estimators_]
def jeremy_trick_RF_sample_size(n):
# Jeremy's trick; hmm.. this won't work as a separate function?
# def batch_size_for_node_splitting(rs, n_samples):
# forest.check_random_state(rs).randint(0, n_samples, 20000)
# forest._generate_sample_indices = batch_size_for_node_splitting
forest._generate_sample_indices = \
(lambda rs, n_samples: forest.check_random_state(rs).randint(0, n_samples, n))
def jeremy_trick_reset_RF_sample_size():
forest._generate_sample_indices = (lambda rs, n_samples:
forest.check_random_state(rs).randint(0, n_samples, n_samples))
def myround(v,ndigits=2):
if np.isclose(v, 0.0):
return "0"
return format(v, '.' + str(ndigits) + 'f')