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update plot learning curve code #438

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merged 2 commits into from
Dec 6, 2022
Merged

update plot learning curve code #438

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3632c99
handle exceptional case in the plot_learningcurve
HyunjunA Dec 6, 2022
394f099
update to deal with the case when wrong hyper-parameters are given fo…
HyunjunA Dec 6, 2022
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29 changes: 26 additions & 3 deletions machine/learn/skl_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -377,7 +377,7 @@ def generate_results(model, input_data,
model = clf.best_estimator_
else:
print("param_grid else")
# plot_learning_curve(tmpdir,_id, model,features,target,cv,return_times=True)
plot_learning_curve(tmpdir,_id, model,features,target,cv,return_times=True)
model.fit(features, target)


Expand Down Expand Up @@ -514,7 +514,7 @@ def generate_results(model, input_data,
# plot_pca_3d_iris(tmpdir,_id,features,target)

# this
plot_tsne(tmpdir,_id,features,target)
# plot_tsne(tmpdir,_id,features,target)

if type(model).__name__ == 'Pipeline':
step_names = [step[0] for step in model.steps]
Expand Down Expand Up @@ -1094,7 +1094,8 @@ def plot_imp_score(tmpdir, _id, coefs, feature_names, imp_score_type):

def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):


# Plot learning curve
print("Plotting learning curve...")


features = np.array(features)
Expand Down Expand Up @@ -1146,7 +1147,20 @@ def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):
# test_scores_mean = np.mean(test_scores, axis=1)
# test_scores_std = np.std(test_scores, axis=1)

# if train_sizes.tolist() has nan, then replace it with 0
# check if all of train_sizes.tolist has nan

if np.isnan(train_sizes.tolist()).all():
#replace nan with -1
train_sizes = np.nan_to_num(train_sizes, nan=-1)
if np.isnan(train_scores.tolist()).all():
# replace nan with -1
train_scores = np.nan_to_num(train_scores, nan=-1)
if np.isnan(test_scores.tolist()).all():
# replace nan with -1
test_scores = np.nan_to_num(test_scores, nan=-1)

# if train_scores
print('train_sizes.tolist()',train_sizes.tolist())
print('train_scores', train_scores.tolist())
print('test_scores', test_scores.tolist())
Expand All @@ -1162,6 +1176,8 @@ def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):


def plot_pca_2d(tmpdir,_id,features,target):

print("plot_pca_2d")
# import numpy as np
# import matplotlib.pyplot as plt

Expand Down Expand Up @@ -1463,6 +1479,8 @@ def plot_tsne(tmpdir,_id,features,target):
# print(X)
# print(y)

print("Plotting t-SNE")

tsne = TSNE(n_components=2, verbose=1, random_state=123)
X_2d = tsne.fit_transform(X)

Expand Down Expand Up @@ -1498,7 +1516,12 @@ def plot_tsne(tmpdir,_id,features,target):
# path = tmpdir + _id + '/tsneJson_' + _id + '.json'
# import json

# X_2d
print("X_2d",X_2d)
print("y",y)

X_2d = [1]
y = [1]

# save X and y to json file
tsne_dict = {
Expand Down