def dependence3d(self, forest, train, feature_set):
print "******************this is the output of dependences of features"
fig = plt.figure()
pdp, (x_axis, y_axis) = partial_dependence(forest,
feature_set,
X=train)
XX, YY = np.meshgrid(x_axis, y_axis)
Z = pdp.T.reshape(XX.shape)
ax = Axes3D(fig)
surf = ax.plot_surface(XX,
YY,
Z,
rstride=1,
cstride=1,
cmap=plt.cm.BuPu)
#------------------------------- ax.set_xlabel(names[target_feature[0]])
#------------------------------- ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('Partial dependence of house value on me12dian age and '
'average occupancy')
plt.subplots_adjust(top=0.9)
plt.show()
def main():
cal_housing = fetch_california_housing()
# split 80/20 train-test
X_train, X_test, y_train, y_test = train_test_split(cal_housing.data,
cal_housing.target,
test_size=0.2,
random_state=1)
names = cal_housing.feature_names
print('_' * 80)
print("Training GBRT...")
clf = GradientBoostingRegressor(n_estimators=100,
max_depth=4,
learning_rate=0.1,
loss='huber',
random_state=1)
clf.fit(X_train, y_train)
print("done.")
print('_' * 80)
print('Convenience plot with ``partial_dependence_plots``')
print
features = [0, 5, 1, 2, (5, 1)]
fig, axs = plot_partial_dependence(clf,
X_train,
features,
feature_names=names,
n_jobs=3,
grid_resolution=50)
fig.suptitle(
'Partial dependence of house value on nonlocation features for the California housing dataset'
)
plt.subplots_adjust(top=0.9)
print('_' * 80)
print('Custom 3d plot via ``partial_dependence``')
print
fig = plt.figure()
target_feature = (1, 5)
pdp, (x_axis, y_axis) = partial_dependence(clf,
target_feature,
X=X_train,
grid_resolution=50)
XX, YY = np.meshgrid(x_axis, y_axis)
Z = pdp.T.reshape(XX.shape).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu)
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle(
'Partial dependence of house value on median age and average occupancy'
)
plt.subplots_adjust(top=0.9)
plt.show()
def compute_f_vals(gbm, model_inds, arr, inds):
feat_vals, feat_val_counts = unique_rows_with_counts(arr[:, inds])
uncentd_f_vals = partial_dependence.partial_dependence(
gbm, model_inds[(inds, )], grid=feat_vals)[0][0]
mean_uncentd_f_val = np.dot(feat_val_counts, uncentd_f_vals) / arr.shape[0]
f_vals = uncentd_f_vals - mean_uncentd_f_val
return dict(zip(map(tuple, feat_vals), f_vals))
def test_partial_dependence_classifier():
# Test partial dependence for classifier
clf = GradientBoostingClassifier(n_estimators=10, random_state=1)
clf.fit(X, y)
pdp, axes = partial_dependence(clf, [0], X=X, grid_resolution=5)
# only 4 grid points instead of 5 because only 4 unique X[:,0] vals
assert pdp.shape == (1, 4)
assert axes[0].shape[0] == 4
# now with our own grid
X_ = np.asarray(X)
grid = np.unique(X_[:, 0])
pdp_2, axes = partial_dependence(clf, [0], grid=grid)
assert axes is None
assert_array_equal(pdp, pdp_2)
def test_partial_dependence_classifier():
# Test partial dependence for classifier
clf = GradientBoostingClassifier(n_estimators=10, random_state=1)
clf.fit(X, y)
pdp, axes = partial_dependence(clf, [0], X=X, grid_resolution=5)
# only 4 grid points instead of 5 because only 4 unique X[:,0] vals
assert pdp.shape == (1, 4)
assert axes[0].shape[0] == 4
# now with our own grid
X_ = np.asarray(X)
grid = np.unique(X_[:, 0])
pdp_2, axes = partial_dependence(clf, [0], grid=grid)
assert axes is None
assert_array_equal(pdp, pdp_2)
def main():
# fetch California housing dataset
try:
cal_housing = fetch_california_housing()
except HTTPError:
print("Failed downloading california housing data.")
return
# split 80/20 train-test
X_train, X_test, y_train, y_test = train_test_split(cal_housing.data,
cal_housing.target,
test_size=0.2,
random_state=1)
names = cal_housing.feature_names
print('_' * 80)
print("Training GBRT...")
clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,
learning_rate=0.1, loss='huber',
random_state=1)
clf.fit(X_train, y_train)
print("done.")
print('_' * 80)
print('Convenience plot with ``partial_dependence_plots``')
print
features = [0, 5, 1, 2, (5, 1)]
fig, axs = plot_partial_dependence(clf, X_train, features, feature_names=names,
n_jobs=3, grid_resolution=50)
fig.suptitle('Partial dependence of house value on nonlocation features\n'
'for the California housing dataset')
plt.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print('_' * 80)
print('Custom 3d plot via ``partial_dependence``')
print
fig = plt.figure()
target_feature = (1, 5)
pdp, (x_axis, y_axis) = partial_dependence(clf, target_feature,
X=X_train, grid_resolution=50)
XX, YY = np.meshgrid(x_axis, y_axis)
Z = pdp.T.reshape(XX.shape).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu)
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('Partial dependence of house value on median age and '
'average occupancy')
plt.subplots_adjust(top=0.9)
plt.show()
def test_partial_dependence_regressor():
# Test partial dependence for regressor
clf = GradientBoostingRegressor(n_estimators=10, random_state=1)
clf.fit(boston.data, boston.target)
grid_resolution = 25
pdp, axes = partial_dependence(
clf, [0], X=boston.data, grid_resolution=grid_resolution)
assert pdp.shape == (1, grid_resolution)
assert axes[0].shape[0] == grid_resolution
def score(self, X_test, y_test):
self.baseline = [self.baseline] * len(y_test)
baseline_MSE = np.sqrt(mean_squared_error(y_test, self.baseline))
print('Baseline Root Mean Squared Error:', round(baseline_MSE, 0))
model_MSE = np.sqrt(mean_squared_error(y_test, self.y_pred))
print('Model Root Mean Squared Error:', round(model_MSE, 0))
print('Reduction in RMSE: %',
((model_MSE - baseline_MSE) / baseline_MSE))
### PD PLOT
importances = self.model.feature_importances_
sorted_imps = sorted(importances)[::-1]
indicies = np.argsort(importances)[::-1]
names = self.X.columns[indicies]
N_COLS = 3
pd_plots = [
partial_dependence(self.model,
target_feature,
X=self.X,
grid_resolution=50)
for target_feature in indicies
]
pd_plots = list(
zip(([pdp[0][0] for pdp in pd_plots]),
([pdp[1][0] for pdp in pd_plots])))
fig, axes = plt.subplots(nrows=3,
ncols=N_COLS,
sharey=True,
figsize=(12.0, 8.0))
for i, (y_axis, x_axis) in enumerate(pd_plots[0:(3 * N_COLS)]):
ax = axes[i // N_COLS, i % N_COLS]
ax.plot(x_axis, y_axis, color="purple")
ax.set_xlim([np.min(x_axis), np.max(x_axis)])
text_x_pos = np.min(x_axis) + 0.05 * (np.max(x_axis) -
np.min(x_axis))
ax.text(text_x_pos,
7.5,
"Feature Importance " + str(round(sorted_imps[i], 2)),
fontsize=12,
alpha=0.7)
ax.set_xlabel(names[i])
ax.grid()
plt.suptitle(
"Partial Dependence Plots (Ordered by Feature Importance)",
fontsize=16)
plt.tight_layout(rect=[0, 0.03, 1, 0.95])
def multi_case_partial_dependence(df, cases, ests, stdzrs,
n_oversamps, c_true, c_pred):
y_true_l = []
y_hat_l = []
y_proba_l = []
feats_l = []
fig, ax = plt.subplots(1,1,figsize=(6,4))
for case, est, stdzr, n, c_t, c_p in zip(cases, ests, stdzrs,
n_oversamps, c_true, c_pred):
data_df = df.copy() # copy to read all columns after dropping
print('case: {}'.format(case[0]))
# drop other binary and probability column
c_drop = [c for c in list(df.columns) if case[1] in c]
data_df.drop(c_drop, axis=1, inplace=True)
# train test split in time
X_train, y_train, X_test, y_test = train_test_split_time(data_df,
'2016-06-01', case[0])
names = list(X_train.columns)
features = [11, 12, 13, 14, (9, 18)]
# plot
fig, axs = plot_partial_dependence(est, X_train, features,
feature_names=names,
n_jobs=3, grid_resolution=50)
fig.suptitle('Partial dependence of features\n'
'for {} model'.format(case[0]))
plt.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print('Custom 3d plot via ``partial_dependence``')
fig = plt.figure()
target_feature = (9, 18)
pdp, axes = partial_dependence(est, target_feature,
X=X_train, grid_resolution=50)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].reshape(list(map(np.size, axes))).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1,
cmap=plt.cm.BuPu, edgecolor='k')
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('Partial dependence of features\n'
'for {} model'.format(case[0]))
plt.subplots_adjust(top=0.9)
plt.show()
def test_partial_dependence_multiclass():
# Test partial dependence for multi-class classifier
clf = GradientBoostingClassifier(n_estimators=10, random_state=1)
clf.fit(iris.data, iris.target)
grid_resolution = 25
n_classes = clf.n_classes_
pdp, axes = partial_dependence(
clf, [0], X=iris.data, grid_resolution=grid_resolution)
assert pdp.shape == (n_classes, grid_resolution)
assert len(axes) == 1
assert axes[0].shape[0] == grid_resolution
def partial_dependency_uncertainty(self, features, grid, percentiles):
# Returns standard deviation of partial dependency curve
pdps_cv = np.zeros((len(grid), len(self.models)), dtype=np.float64)
# logging.debug('grid: {}; shape: {}'.format(grid, grid.shape))
for i, cv_model in enumerate(self.models):
pdps = skl_e_pd.partial_dependence(cv_model,
features,
grid,
percentiles=percentiles)
pdps_cv[:, i] = pdps[0]
stds = np.std(pdps_cv, axis=1)
return stds
def test_partial_dependence_classifier():
# Test partial dependence for classifier
clf = GradientBoostingClassifier(n_estimators=10, random_state=1)
clf.fit(X, y)
pdp, axes = partial_dependence(clf, [0], X=X, grid_resolution=5)
# only 4 grid points instead of 5 because only 4 unique X[:,0] vals
assert pdp.shape == (1, 4)
assert axes[0].shape[0] == 4
# now with our own grid
X_ = np.asarray(X)
grid = np.unique(X_[:, 0])
pdp_2, axes = partial_dependence(clf, [0], grid=grid)
assert axes is None
assert_array_equal(pdp, pdp_2)
# with trivial (no-op) sample weights
clf.fit(X, y, sample_weight=np.ones(len(y)))
pdp_w, axes_w = partial_dependence(clf, [0], X=X, grid_resolution=5)
assert pdp_w.shape == (1, 4)
assert axes_w[0].shape[0] == 4
assert_allclose(pdp_w, pdp)
# with non-trivial sample weights
clf.fit(X, y, sample_weight=sample_weight)
pdp_w2, axes_w2 = partial_dependence(clf, [0], X=X, grid_resolution=5)
assert pdp_w2.shape == (1, 4)
assert axes_w2[0].shape[0] == 4
assert np.all(np.abs(pdp_w2 - pdp_w) / np.abs(pdp_w) > 0.1)
def calc_avg_pdp(clf): from sklearn.ensemble.partial_dependence import partial_dependence import pandas pdps = np.empty(clf.fit_clfs.shape + (clf.feature_importances.shape[2], 2, 100)) for i in range(0, clf.mask_num): for j in range(0, clf.mask_num): if i == j: pdps[i, j] = None else: for feature in range(0, clf.feature_importances.shape[2]): pdp, a = partial_dependence(clf.fit_clfs[i, j], [feature], X=clf.c_data[i, j][0]) pdps[i, j, feature] = [pdp[0], a[0]] clf.pdps = np.ma.masked_array(pdps, mask= pandas.isnull(pdps))
def calc_avg_pdp(clf):
from sklearn.ensemble.partial_dependence import partial_dependence
import pandas
pdps = np.empty(clf.fit_clfs.shape +
(clf.feature_importances.shape[2], 2, 100))
for i in range(0, clf.mask_num):
for j in range(0, clf.mask_num):
if i == j:
pdps[i, j] = None
else:
for feature in range(0, clf.feature_importances.shape[2]):
pdp, a = partial_dependence(clf.fit_clfs[i, j], [feature],
X=clf.c_data[i, j][0])
pdps[i, j, feature] = [pdp[0], a[0]]
clf.pdps = np.ma.masked_array(pdps, mask=pandas.isnull(pdps))
def plot_partial_dependence_with_unc(self,
gbm,
feature_idx,
percentiles=(0.05, 0.95),
absolute_yscale=False,
absolute_yticks=True,
**fig_params):
outcome_mean = np.mean(self.train_y)
fig = plt.figure(**fig_params)
ax = fig.add_subplot(1, 1, 1)
pdps, axes = skl_e_pd.partial_dependence(gbm, [feature_idx],
X=self.train_X,
percentiles=percentiles)
if absolute_yscale or absolute_yticks:
pdps = pdps + outcome_mean
# plt.xticks(locs, labels)
stds = self.partial_dependency_uncertainty([feature_idx],
grid=axes[0],
percentiles=percentiles)
pdp_uncertainty_plot = ax.fill_between(axes[0],
pdps[0] - stds,
pdps[0] + stds,
alpha=0.2,
color=self.pdp_color)
pdp_plot, = ax.plot(axes[0], pdps[0], lw=5, color=self.pdp_color)
if absolute_yscale:
c_ylim = ax.get_ylim()
ax.set_ylim(0, c_ylim[1])
# if offset_mean and not offset_mean_labels:
# # fig.canvas.draw()
# ax.set_yticklabels(ax.get_yticks())
# labels_both = ax.get_yticklabels(which='both')
# for l in labels_both:
# l.set_text('{:.2f}'.format(outcome_mean + float(l.get_text())))
# ax.set_yticklabels(labels_both)
return fig, ax, pdp_plot, pdp_uncertainty_plot
def dependence3d(self, forest, train, feature_set):
print "******************this is the output of dependences of features"
fig = plt.figure()
pdp, (x_axis, y_axis) = partial_dependence(forest, feature_set,
X=train)
XX, YY = np.meshgrid(x_axis, y_axis)
Z = pdp.T.reshape(XX.shape)
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu)
#------------------------------- ax.set_xlabel(names[target_feature[0]])
#------------------------------- ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('Partial dependence of house value on median age and '
'average occupancy')
plt.subplots_adjust(top=0.9)
plt.show()
def test_partial_dependence_sample_weight():
# Test near perfect correlation between partial dependence and diagonal
# when sample weights emphasize y = x predictions
N = 1000
rng = np.random.RandomState(123456)
mask = rng.randint(2, size=N, dtype=bool)
x = rng.rand(N)
# set y = x on mask and y = -x outside
y = x.copy()
y[~mask] = -y[~mask]
X = np.c_[mask, x]
# sample weights to emphasize data points where y = x
sample_weight = np.ones(N)
sample_weight[mask] = 1000.
clf = GradientBoostingRegressor(n_estimators=10, random_state=1)
clf.fit(X, y, sample_weight=sample_weight)
grid = np.arange(0, 1, 0.01)
pdp = partial_dependence(clf, [1], grid=grid)
assert np.corrcoef(np.ravel(pdp[0]), grid)[0, 1] > 0.99
def plot_3d(self,
feature_3d,
top=0.9,
grid_resolution=50,
rstride=1,
cstride=1,
cmap="jet",
edgecolor='K',
elev=22,
azim=122):
fig = plt.figure()
pdp, axes = partial_dependence(gbrt=self.model,
target_variables=feature_3d,
X=self.feature_df,
grid_resolution=grid_resolution)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].reshape(list(map(np.size, axes))).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX,
YY,
Z,
rstride=rstride,
cstride=cstride,
cmap=cmap,
edgecolor=edgecolor)
ax.set_xlabel(self.feature_list[feature_3d[0]])
ax.set_ylabel(self.feature_list[feature_3d[1]])
ax.set_zlabel('Partial dependence')
ax.view_init(elev=elev, azim=azim)
plt.colorbar(surf)
plt.suptitle('Partial dependence of house value on median\n'
'age and average occupancy')
plt.subplots_adjust(top=top)
plt.savefig(os.path.join(self.output_path, self.fig_file))
plt.close()
priors = joblib.load(project_dir + '/data/raw/%s_priors' % prior_group)
stacked_clf = joblib.load(target_dir + prior_group + '_' + method +
'_final_stacked_clf_' + str(k) + '.npy')
X_train = joblib.load(target_dir + prior_group + '_' + method +
'_final_predictions_' + str(k) + '.npy')
for x in [9]:
for y in [25]:
target_feature = (x, y)
fig = plt.figure()
names = [priors[target_feature[0]], priors[target_feature[1]]]
print(
'Convenience plot with ``partial_dependence_plots`` for %s and %s'
% (names[0], names[1]))
pdp, axes = partial_dependence(stacked_clf,
target_feature,
X=X_train,
grid_resolution=50)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].reshape(list(map(np.size, axes))).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX,
YY,
Z,
rstride=1,
cstride=1,
cmap=plt.cm.BuPu)
ax.set_xlabel(names[0], fontsize=12)
ax.set_ylabel(names[1], fontsize=12)
ax.set_zlabel('Partial dependence', fontsize=12)
ax.view_init(elev=12, azim=-142)
plt.xticks([0, 0.5, 1])
print
features = [0, 5, 1, 2, (5, 1)]
fig, axs = plot_partial_dependence(clf, X_train, features, feature_names=names,
n_jobs=3, grid_resolution=50)
fig.suptitle('Partial dependence of house value on nonlocation features\n'
'for the California housing dataset')
pl.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print('_' * 80)
print('Custom 3d plot via ``partial_dependence``')
print
fig = pl.figure()
target_feature = (1, 5)
pdp, (x_axis, y_axis) = partial_dependence(clf, target_feature,
X=X_train, grid_resolution=50)
XX, YY = np.meshgrid(x_axis, y_axis)
Z = pdp.T.reshape(XX.shape).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=pl.cm.BuPu)
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
pl.colorbar(surf)
pl.suptitle('Partial dependence of house value on median age and '
'average occupancy')
pl.subplots_adjust(top=0.9)
pl.show()
features,
feature_names=names,
n_jobs=3,
grid_resolution=50)
fig.suptitle('Partial dependence of house value on nonlocation features\n'
'for the California housing dataset')
pl.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print('_' * 80)
print('Custom 3d plot via ``partial_dependence``')
print
fig = pl.figure()
target_feature = (1, 5)
pdp, (x_axis, y_axis) = partial_dependence(clf,
target_feature,
X=X_train,
grid_resolution=50)
XX, YY = np.meshgrid(x_axis, y_axis)
Z = pdp.T.reshape(XX.shape).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=pl.cm.BuPu)
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
pl.colorbar(surf)
pl.suptitle('Partial dependence of house value on median age and '
'average occupancy')
pl.subplots_adjust(top=0.9)
def main():
# ommit gender and children (as does the reference paper)
X = df[["yearsmarried", "age", "religiousness", "occupation",
"rating"]].values
y = df['affairs'].values
# shuffle data
order = np.argsort(np.random.random(y.shape))
X = X[order]
y = y[order]
names = ["yearsmarried", "age", "religiousness", "occupation", "rating"]
print("features to be plotted on first graph: " + str(names))
clf = GradientBoostingRegressor(n_estimators=100,
max_depth=4,
learning_rate=0.1,
loss='huber',
random_state=1)
clf.fit(X, y)
print('Convenience plot with ``partial_dependence_plots``')
# features = [0, 5, 1, 2, (5, 1)]
features = [0, 1, 2, 3, 4, (0, 1)]
fig, axs = plot_partial_dependence(clf,
X,
features,
feature_names=names,
n_jobs=-1,
grid_resolution=100,
n_cols=3)
fig.set_size_inches(10.5, 7.5)
fig.suptitle('Partial dependence for amount of affairs\n'
'for the Affairs dataset.')
plt.subplots_adjust(top=0.9) # tight_layout causes overlap
print('Custom 3d plot via ``partial_dependence``')
fig = plt.figure()
target_feature = (0, 1)
pdp, axes = partial_dependence(clf,
target_feature,
X=X,
grid_resolution=100)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].reshape(list(map(np.size, axes))).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX,
YY,
Z,
rstride=1,
cstride=1,
cmap=plt.cm.BuPu,
edgecolor='k')
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('Partial dependence of amount of affairs\n'
'for the amount of years married and the age.')
plt.subplots_adjust(top=0.9)
plt.show()
####################################################################
# Inspect feature 0, 3, 5, 6, and the interaction between 5 and 0, and 5
# and 3
target_features = [0, 3, 5, 6, (5, 0), (5, 3)]
fig, axs = plot_partial_dependence(clf, X_train, target_features,
feature_names=boston.feature_names,
grid_resolution=30)
plt.tight_layout()
####################################################################
# Lower-level partial_dependence function
# ----------------------------------------
target_feature = (5, 0)
from sklearn.ensemble.partial_dependence import partial_dependence
partial_deps, grid = partial_dependence(clf, target_feature,
X=X_train, grid_resolution=50)
import numpy as np
# The 2D coordinate grid (for plotting)
XX, YY = np.meshgrid(grid[0], grid[1])
# Reshape the partial deps on the grid
Z = partial_deps[0].reshape(list(map(np.size, grid))).T
####################################################################
# 3D plotting
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1,
cmap=plt.cm.BuPu, edgecolor='k')
clf = GradientBoostingClassifier(n_estimators=1000, learning_rate=0.1,
max_depth=3, random_state=0).fit(X_train[X_train_relevant.columns], y_train)
#%%
names = X_train_relevant.columns
features = [0,1 , (1, 2)]
fig, axs = plot_partial_dependence(clf,
X_train[X_train_relevant.columns],
features,
feature_names=names)
#%%
target_feature = (0, 1)
pdp, axes = partial_dependence(clf, target_feature,
X=X_train[X_train_relevant.columns], grid_resolution=50)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].reshape(list(map(np.size, axes))).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1,
cmap=plt.cm.BuPu, edgecolor='k')
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('Partial dependence of house value on median\n'
'age and average occupancy')
plt.subplots_adjust(top=0.9)
def ml_GradientBoostingClassifier2(self):
# This example shows how to obtain partial dependence plots from a GradientBoostingRegressor trained on the California housing dataset.
cal_housing = fetch_california_housing()
# split 80/20 train-tests
X_train, X_test, y_train, y_test = train_test_split(cal_housing.data,
cal_housing.target,
test_size=0.2,
random_state=1)
names = cal_housing.feature_names
print("Training GBRT...")
clf = GradientBoostingRegressor(n_estimators=100,
max_depth=4,
learning_rate=0.1,
loss='huber',
random_state=1)
clf.fit(X_train, y_train)
print(" done.")
print('Convenience plot with ``partial_dependence_plots``')
features = [0, 5, 1, 2, (5, 1)]
fig, axs = plot_partial_dependence(clf,
X_train,
features,
feature_names=names,
n_jobs=3,
grid_resolution=50)
fig.suptitle(
'Partial dependence of house value on nonlocation features\n'
'for the California housing dataset')
plt.subplots_adjust(
top=0.9) # tight_layout causes overlap with suptitle
print('Custom 3d plot via ``partial_dependence``')
fig = plt.figure()
target_feature = (1, 5)
pdp, axes = partial_dependence(clf,
target_feature,
X=X_train,
grid_resolution=50)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].reshape(list(map(np.size, axes))).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX,
YY,
Z,
rstride=1,
cstride=1,
cmap=plt.cm.BuPu,
edgecolor='k')
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('Partial dependence of house value on median\n'
'age and average occupancy')
plt.subplots_adjust(top=0.9)
plt.show()
The number of columns in the grid plot (default: 3).
percentiles : (low, high), default=(0.05, 0.95)
The lower and upper percentile used to create the extreme values
for the PDP axes.
grid_resolution : int, default=100
The number of equally spaced points on the axes.
"""
my_plots = plot_partial_dependence(gbrt=my_model,
features=[0,1,2],
X=imputed_X,
feature_names=cols_to_use,
grid_resolution=100,
n_cols=2)
partial_dependence?
"""
plot yourself with seaborn...
partial_dependence(gbrt, target_variables, grid=None,
X=None, percentiles=(0.05, 0.95),
grid_resolution=100)
"""
p_dep = partial_dependence(gbrt=my_model,
target_variables=[0,1,2],
X=imputed_X,
percentiles=(0.05,0.95))
p_dep[0][0] #y or PDP
p_dep[1][0] #x for Distance
p_dep[1][1] #x for Land
p_dep[1][2] #x for BuildingArea
#a 25 = 26-1 ami az y számossága eggyel csökkentve az 0tól kezdődő számossága miatt
label = len(ytrain.value_counts())
pdp_fig, pdp_axs = partial_dependence.plot_partial_dependence(gbc,Xtrain,feature_numbers,feature_names, label)
plt.subplots_adjust(top=1.5) # tight_layout causes overlap with suptitle
#%%
from mpl_toolkits.mplot3d import Axes3D
print('Custom 3d plot via ``partial_dependence``')
fig = plt.figure()
target_feature = (3,7)
pdp, axes = partial_dependence.partial_dependence(gbc, target_feature,X=Xtrain, grid_resolution=100, )
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].reshape(list(map(np.size, axes))).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1,
cmap=plt.cm.BuPu, edgecolor='k')
ax.set_xlabel(feature_names[target_feature[0]])
ax.set_ylabel(feature_names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.subplots_adjust(top=0.9)
plt.show()
def make_pdp(i, mean=0.0):
pdp = partial_dependence(self.model, i, X=X, grid_resolution=100)
feature = self.feature_names[i]
feature_bins = [denorm.denormalize_feature_value(feature, x) for x in list(pdp[1][0])]
data = list([x - mean for x in pdp[0]])
return {"feature": feature, "featureBins": feature_bins, "data": data}
def partial_dependency_catagorical_plot(self,
gbm,
feature_label,
ax_limits_per_feature=None,
overlay_box_plots=False,
add_means=True,
absolute_yscale=False,
absolute_yticks=True,
**fig_params):
"""Plots partial dependency for a categorical variable.
arguments:
absolute_yscale - absolute_scale (starts at zero) on y axis
absolute_yticks - flag to subtract mean from y axis pabels
Todo: split this methods into 2-3 smaller ones
"""
legends = []
legend_labels = []
deltas = []
deltas_unc = []
labels = []
means = []
means_unc = []
raw_data = []
counts = []
raw_stds = []
width = 0.8
if add_means:
width = 0.35
if ax_limits_per_feature is None:
ax_limits_per_feature = {}
outcome_mean = np.mean(self.train_y)
for feature_index in self.categorical_features_ind[feature_label]:
y, x = skl_e_pd.partial_dependence(gbm, [feature_index],
X=self.train_X)
stds = self.partial_dependency_uncertainty([feature_index],
grid=x[0],
percentiles=(0.0, 1.0))
if len(x) == 0 or len(y) == 0:
logging.debug(
'no results for feature_index {}'.format(feature_index))
else:
try:
delta = y[0][np.where(x[0] == 1)[0][0]]\
- y[0][np.where(x[0] == 0)[0][0]]
# if absolute_yscale or absolute_yticks:
# logging.debug('original delta: {}'.format(delta))
# delta += outcome_mean
# logging.debug('original delta with mean: {}'.format(delta))
deltas.append(delta)
labels.append(self.feature_labels[feature_index].replace(
feature_label + '_', ''))
deltas_unc.append(np.sqrt(np.sum(stds**2)))
train_X_idx = self.train_X[
self.feature_labels[feature_index]] == 1
raw_data_subset = self.train_y[train_X_idx]
# if not absolute_yscale and not absolute_yticks:
# raw_data_subset -= outcome_mean
raw_data_weights = self.train_weights[train_X_idx]
raw_data.append(raw_data_subset)
means.append(
np.average(raw_data_subset, weights=raw_data_weights))
means_unc.append(
self.mean_uncertainty(raw_data_subset,
weights=raw_data_weights))
counts.append(int(np.sum(raw_data_weights)))
raw_stds.append(
pde.std(raw_data_subset, weights=raw_data_weights))
except Exception as e:
logging.error('Cannot get partial dependence delta for \
feature index "{}" due to "{}"'.format(feature_index, e))
idxs = np.argsort(deltas)
plot_x = np.arange(idxs.shape[0]) + 0.5
plot_deltas = np.array(deltas)[idxs]
fig = plt.figure(**fig_params)
ax = fig.add_subplot(1, 1, 1)
if absolute_yscale or not absolute_yticks:
bar_bottom = 0
else:
bar_bottom = outcome_mean
logging.debug('bar bottom: {}, plot_deltas: {}'.format(
bar_bottom, plot_deltas))
pdp_bars = ax.bar(plot_x,
plot_deltas,
width=width,
align='center',
color=self.pdp_color,
bottom=bar_bottom)
legends.append(pdp_bars)
legend_labels.append('Partial dependences with uncertainties')
ax.errorbar(plot_x,
plot_deltas + bar_bottom,
yerr=np.array(deltas_unc)[idxs],
color='grey',
fmt='o')
if add_means:
means_y = np.array(means)[idxs]
if not absolute_yticks:
means_y -= outcome_mean
logging.debug('bar bottom: {}, means_y: {}'.format(
bar_bottom, means_y))
means_bars = ax.bar(plot_x + width,
means_y - bar_bottom,
width=width,
align='center',
alpha=0.5,
color=self.means_color,
bottom=bar_bottom)
ax.errorbar(plot_x + width,
means_y,
yerr=np.array(means_unc)[idxs],
color='grey',
fmt='o')
legends.append(means_bars)
legend_labels.append('Raw averages with uncertainties')
# ax.legend(['Raw averages with uncertainties'])
ax.grid(alpha=0.3)
if overlay_box_plots:
self.box_plots_raw_data(raw_data, plot_x)
# store x,y limits based on primary data
xlim = ax.get_xlim()
ylim = ax.get_ylim()
counts_bars = self.overlay_counts_histogram(ax, plot_x, counts, xlim,
ylim)
if counts_bars is not None:
legends.append(counts_bars)
legend_labels.append('Counts')
plt.xticks(plot_x, np.array(labels)[idxs], rotation='vertical')
plt.xlabel('{}'.format(feature_label))
plt.ylabel(self.outcome_ylabel(absolute_yticks))
plt.title('Partial dependence on {} ({} trees)'.format(
feature_label, gbm.n_estimators))
ylim = ax_limits_per_feature.get('ylim')
if ylim is not None:
ax.set_ylim(tuple(ylim))
xlim = ax_limits_per_feature.get('xlim')
if xlim is not None:
ax.set_ylim(tuple(xlim))
logging.debug(
'creating legend with handles: "{}", labels: "{}"'.format(
legends, legend_labels))
try:
ax.legend(handles=legends,
labels=legend_labels,
bbox_to_anchor=(1, 0.5))
except Exception as e:
logging.error(
'Cannot add legend for feature "{}" due to "{}"'.format(
feature_label, e))
# plt.legend(
# handles=legends[:1],
# loc='center left',
# bbox_to_anchor=(1, 0.5))
if self.show_plots:
plt.show()
# plt.show()
self.save_fig(fig, 'partial_dependence_{}.png'.format(feature_label))
df = pd.DataFrame(
[labels, deltas, deltas_unc, means, means_unc, counts, raw_stds],
index=[
feature_label, 'partial dependence delta',
'partial dependence delta uncertainty', 'mean',
'mean uncertainty', 'sample size', 'standard deviation'
]).T.sort_values(by='partial dependence delta')
df.to_excel(
self.destination_dir +
'partial_dependence_with_stats_{}.xlsx'.format(feature_label))
print('Convenience plot with ``partial_dependence_plots``')
features = [0, 5, 1, 2, (5, 1)]
fig, axs = plot_partial_dependence(clf, X_train, features,
feature_names=names,
n_jobs=1, grid_resolution=50)
fig.suptitle('Partial dependence of house value on nonlocation features\n'
'for the California housing dataset')
plt.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print('Custom 3d plot via ``partial_dependence``')
fig = plt.figure()
target_feature = (1, 5)
pdp, axes = partial_dependence(clf, target_feature,
X=X_train, grid_resolution=50)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].reshape(list(map(np.size, axes))).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1,
cmap=plt.cm.BuPu, edgecolor='k')
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('Partial dependence of house value on median\n'
'age and average occupancy')
plt.subplots_adjust(top=0.9)
my_model,
features=[0, 2], # column numbers of plots we want to show
X=train_X, # raw predictors data.
feature_names=names, # labels on graphs
grid_resolution=10) # number of values to plot on x axis
fig.suptitle('Partial dependence of house value on nonlocation features\n'
'for the Melbourne housing dataset')
plt.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print('Custom 3d plot via ``partial_dependence``')
fig = plt.figure()
target_feature = (0, 2)
pdp, axes = partial_dependence(my_model,
target_feature,
X=train_X,
grid_resolution=50)
XX, YY = np.meshgrid(axes[0], axes[1])
Z = pdp[0].reshape(list(map(np.size, axes))).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX,
YY,
Z,
rstride=1,
cstride=1,
cmap=plt.cm.BuPu,
edgecolor='k')
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
def brt_train_plot_func(X, y, feats, train_model=False):
"""
Function to train the BRT model and plots to make inference
"""
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.15,
random_state=1)
#y_train = y_train.as_matrix().ravel()
#y_test = y_test.as_matrix().ravel() using only when coming from pandas dataframe
param_grid_brt = {
'learning_rate': np.logspace(-4, 0, 50),
'max_depth': range(2, 8),
'min_samples_leaf': range(3, 10)
}
#from sklearn.metrics import mean_squared_error, make_scorer
#param_grid_brt = {'learning_rate': np.logspace(-4,0,3),'max_depth': [2,6],'min_samples_leaf': [3,10]}
clf = GradientBoostingRegressor(n_estimators=500)
#cross-validation grid to search the best parameters
#str_in = raw_input("(T)raining or (U)sed selected (Default: U): ")
if train_model:
print "Training model"
#mse_scorer = make_scorer(mean_squared_error,greater_is_better = False)
brt_complete = GridSearchCV(clf,
param_grid_brt,
n_jobs=-1,
verbose=True,
cv=10)
brt_complete.fit(X_train, y_train)
brt = brt_complete.best_estimator_
else:
brt = GradientBoostingRegressor(n_estimators=2000,
learning_rate=0.0008,
max_depth=4,
min_samples_leaf=5)
brt.fit(X_train, y_train)
#str_in = raw_input("Descomp-(T)raining or (U)sed selected (Default: U): ")
#
#if str_in == 'T':
# print "Training descomp model"
# brt_descomp_complete = GridSearchCV(clf_descomp, param_grid_brt,n_jobs = -1,verbose = True,cv = 10)
# brt_descomp_complete.fit(X_descomp_train,y_descomp_train)
# brt_descomp = brt_descomp_complete.best_estimator_
#else:
# brt_descomp = GradientBoostingRegressor(n_estimators=2000,learning_rate=0.006,max_depth = 4,min_samples_leaf=5)
# brt_descomp.fit(X_descomp_train,y_descomp_train)
plt.close('all')
# ####### IAM %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#relative importance
feature_importance = brt.feature_importances_
# make importances relative to max importance
feature_importance = 100.0 * (feature_importance /
feature_importance.max())
sorted_idx = np.argsort(feature_importance)
pos = np.arange(sorted_idx.shape[0]) + .5
#plt.sca(axs[5])
#plt.cla()
#feats = np.array(features)
plt.barh(pos, feature_importance[sorted_idx], align='center')
plt.yticks(pos, feats[sorted_idx], fontsize=20)
plt.title("TS Group 3", fontsize=20)
plt.xlabel('Relative Importance (%)', fontsize=20)
plt.subplots_adjust(top=0.9, left=0.18, bottom=0.15)
#partial dependence plot
#mse
from sklearn.metrics import mean_squared_error, r2_score
y_pred = brt.predict(X_test)
print "MSE", mean_squared_error(y_test, y_pred)
print 'R2', r2_score(y_test, y_pred)
#plot for IAM
#plt.figure()
#4 features AVNN, age, sex, ci
#features = ['SDNN','HRVTriangIndex','SDSD','AVNN','logIndex','RMSSD','ci','sex','age']
#target_features = [features[3],features[-1],features[-2],features[-3]]
target_features_idx = [0, 4, 7, 3, 9, (0, 4)]
fig_hrt, axs = plot_partial_dependence(brt,
X_train,
target_features_idx,
feature_names=feats,
n_jobs=-1,
grid_resolution=80)
fig_hrt.suptitle('TS Group 3 = f(HRV)', fontsize=20)
plt.subplots_adjust(top=0.9, hspace=0.4, wspace=0.5)
for a in range(5):
axs[a].set_ylabel(
"TS", fontsize=20) # tight_layout causes overlap with suptitle
axs[a].set_xlabel(feats[target_features_idx[a]], fontsize=20)
axs[5].set_xlabel(feats[target_features_idx[5][0]], fontsize=20)
axs[5].set_ylabel(feats[target_features_idx[5][1]], fontsize=20)
plt.show()
target_features_idx = [8, 7]
fig_hrt, axs = plot_partial_dependence(brt,
X_train,
target_features_idx,
feature_names=feats,
n_jobs=-1,
grid_resolution=80)
fig_hrt.suptitle('TS Group 3 = f(HRV)', fontsize=20)
plt.subplots_adjust(top=0.9, left=0.12)
for a in range(2):
axs[a].set_ylabel(
"TS partial dependence",
fontsize=20) # tight_layout causes overlap with suptitle
axs[a].set_xlabel(feats[target_features_idx[a]], fontsize=20)
axs[a].set_ylim(-2.5, 1.5)
plt.show()
fig = plt.figure()
target_feature = (7, 3)
pdp, (x_axis, y_axis) = partial_dependence(brt,
target_feature,
X=X_train,
grid_resolution=80)
XX, YY = np.meshgrid(x_axis, y_axis)
Z = pdp.T.reshape(XX.shape).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu)
ax.set_xlabel(feats[target_feature[0]], fontsize=18)
ax.set_ylabel(feats[target_feature[1]], fontsize=18)
ax.set_zlabel('$TS$', fontsize=18)
# pretty init view
ax.view_init(elev=22, azim=122)
plt.colorbar(surf)
plt.suptitle('$TS = f(Scl,TINN)$', fontsize=18)
# features = [0, 5, 1, 2, (5, 1)]
# features = ['MedInc', 'AveOccup', 'HouseAge', 'AveRooms', ('AveOccup', 'HouseAge')]
# fig, axs = plot_partial_dependence(clf, X_train, features, feature_names=names,
# n_jobs=1, grid_resolution=50)
# fig.suptitle('Partial dependence of house value on nonlocation features\n'
# 'for the California housing dataset')
# pl.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print('_' * 80)
print('Custom 3d plot via ``partial_dependence``')
print
fig = pl.figure()
target_feature = (1, 5)
pdp, (x_axis, y_axis) = partial_dependence(clf,
target_feature,
X=X_train,
grid_resolution=50)
XX, YY = np.meshgrid(x_axis, y_axis)
Z = pdp.T.reshape(XX.shape).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=pl.cm.BuPu)
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
pl.colorbar(surf)
pl.suptitle('Partial dependence of house value on median age and '
'average occupancy')
pl.subplots_adjust(top=0.9)
#def plot_feature_importances(model):
# plt.figure(figsize=(8,6))
# n_features = len(names)
# plt.barh(range(n_features), model.feature_importances_, align='center')
# plt.yticks(np.arange(n_features), names)
# plt.xlabel("Feature importance")
# plt.ylabel("Feature")
# plt.ylim(-1, n_features)
#
#plot_feature_importances(gbc)
#
#plt.show()
pd.DataFrame(np.vstack(gbc.feature_importances_).T, columns=names).T.sort_values(by=0).plot(kind='barh')
pdep = partial_dependence(gbc, features, X = X_train, grid_resolution=100)
columns_new = ['Risk Probabilities',names[feat_num]]
fico = pd.DataFrame(np.vstack(pdep).T, columns = columns_new)
fico = fico[fico[names[feat_num]]>600]
fico.plot(names[feat_num],'Risk Probabilities')
# ROC Curve Plot
logit_roc_auc = roc_auc_score(Y_test, gbc.predict(X_test))
fpr, tpr, thresholds = roc_curve(Y_test, gbc.predict_proba(X_test)[:,1])
plt.figure()
plt.plot(fpr, tpr, label='Gradient Boosting Classifier (area = %0.2f)' % logit_roc_auc)
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0.0, 1.0])
# features = [0, 5, 1, 2, (5, 1)]
# features = ['MedInc', 'AveOccup', 'HouseAge', 'AveRooms', ('AveOccup', 'HouseAge')]
# fig, axs = plot_partial_dependence(clf, X_train, features, feature_names=names,
# n_jobs=1, grid_resolution=50)
# fig.suptitle('Partial dependence of house value on nonlocation features\n'
# 'for the California housing dataset')
# pl.subplots_adjust(top=0.9) # tight_layout causes overlap with suptitle
print('_' * 80)
print('Custom 3d plot via ``partial_dependence``')
print
fig = pl.figure()
target_feature = (1, 5)
pdp, (x_axis, y_axis) = partial_dependence(clf, target_feature,
X=X_train, grid_resolution=50)
XX, YY = np.meshgrid(x_axis, y_axis)
Z = pdp.T.reshape(XX.shape).T
ax = Axes3D(fig)
surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=pl.cm.BuPu)
ax.set_xlabel(names[target_feature[0]])
ax.set_ylabel(names[target_feature[1]])
ax.set_zlabel('Partial dependence')
# pretty init view
ax.view_init(elev=22, azim=122)
pl.colorbar(surf)
pl.suptitle('Partial dependence of house value on median age and '
'average occupancy')
pl.subplots_adjust(top=0.9)
pl.show()
'learning_rate': 0.01,
'loss': 'ls'
}
gbr = GradientBoostingRegressor(**params)
gbr.fit(X_train, y_train)
pd.crosstab(y_test,
gbr.predict(X_test).round(),
rownames=['Actual'],
colnames=['Predicted'])
pd.DataFrame({
'Variable': X_test.columns,
'Importance': gbr.feature_importances_
}).sort_values('Importance', ascending=False)
fig, axs = plot_partial_dependence(
gbr,
X=X_test,
features=['Parhelion Patrol', 'Rubblebelt Boar', 'Hammer Dropper'],
feature_names=feature_list,
n_jobs=1,
grid_resolution=10)
allpd = {}
for i in range(len(feature_list) - 1):
key, values = partial_dependence(gbr, target_variables=i, X=X_test)
allpd.update(dict(zip([feature_list[i]], key.tolist())))
df = pd.DataFrame(dict([(k, pd.Series(v)) for k, v in allpd.items()]))
""" 1.11.4.7.2. Partial dependence """ # from sklearn.datasets import make_hastie_10_2 # from sklearn.ensemble import GradientBoostingClassifier # from sklearn.ensemble.partial_dependence import plot_partial_dependence # X, y = make_hastie_10_2(random_state=0) clf = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, # max_depth=1, random_state=0).fit(X, y) # features = [0, 1, (0, 1)] # fig, axs = plot_partial_dependence(clf, X, features) # from sklearn.datasets import load_iris # iris = load_iris() # mc_clf = GradientBoostingClassifier(n_estimators=10, # max_depth=1).fit(iris.data, iris.target) # features = [3, 2, (3, 2)] # fig, axs = plot_partial_dependence(mc_clf, X, features, label=0) from sklearn.ensemble.partial_dependence import plot_partial_dependence from sklearn.ensemble.partial_dependence import partial_dependence pdp, axes = partial_dependence(clf, [0], X=X) print pdp
