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Update product page and api.rst and skl_util.py on pca, tsne, and learning curve #492

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Update product page and api.rst and skl_util.py on pca, tsne, and learning curve #492

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f99bc7a
update product page
HyunjunA Dec 20, 2022
d0960ae
Add documentations for plot_learning_curve, plot_tsne_2d, and plot_pc…
HyunjunA Dec 20, 2022
9cadfe9
Add plot_learning_curve, plot_pca_2d, and plot_tsne_2d to api.rst
HyunjunA Dec 20, 2022
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6 changes: 6 additions & 0 deletions docs/source/api.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -136,6 +136,12 @@ These methods generate sklearn models and evaluate them.

.. autofunction:: machine.learn.skl_utils.plot_confusion_matrix

.. autofunction:: machine.learn.skl_utils.plot_learning_curve

.. autofunction:: machine.learn.skl_utils.plot_pca_2d

.. autofunction:: machine.learn.skl_utils.plot_tsne_2d

.. autofunction:: machine.learn.skl_utils.plot_roc_curve

.. autofunction:: machine.learn.skl_utils.plot_imp_score
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183 changes: 68 additions & 115 deletions machine/learn/skl_utils.py
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Expand Up @@ -50,6 +50,10 @@
import matplotlib.colors as mcolors
from matplotlib.patches import Patch
from sklearn.manifold import TSNE
from sklearn.model_selection import learning_curve




mpl.use('Agg')

Expand Down Expand Up @@ -503,7 +507,7 @@ def generate_results(model, input_data,
plot_pca_2d(tmpdir,_id,features,target)
# plot_pca_3d(tmpdir,_id,features,target)
# plot_pca_3d_iris(tmpdir,_id,features,target)
plot_tsne(tmpdir,_id,features,target)
plot_tsne_2d(tmpdir,_id,features,target)

if type(model).__name__ == 'Pipeline':
step_names = [step[0] for step in model.steps]
Expand Down Expand Up @@ -1082,10 +1086,27 @@ def plot_imp_score(tmpdir, _id, coefs, feature_names, imp_score_type):
return top_features, indices

def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):
"""Make learning curve.

Parameters
----------
tmpdir: string
Temporary directory for saving experiment results
_id: string
Experiment ID in Aliro
model: user specified model
features: np.darray/pd.DataFrame
Features in training dataset
target: np.darray/pd.DataFrame
Target in training dataset
cv: int, cross-validation generator or an iterable

from sklearn.model_selection import learning_curve
from matplotlib import pyplot as plt
import numpy as np
Returns
-------
None
"""




features = np.array(features)
Expand All @@ -1094,7 +1115,6 @@ def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):
target[target == -1] = 0



train_sizes, train_scores, test_scores, fit_times, _ = learning_curve(model,features,target,None, np.linspace(0.1, 1.0, 5), cv,return_times=True)

plt.xlabel("Training examples")
Expand All @@ -1108,9 +1128,6 @@ def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):

plt.grid()

# print('train_scores_mean',train_scores_mean)
# print('test_scores_mean',test_scores_mean)
# print('train_sizes',train_sizes)

plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
Expand All @@ -1126,16 +1143,12 @@ def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):
plt.title('Learning curve')

plt.legend(loc='best')
# plt.legend(loc="lower right")
plt.savefig(tmpdir + _id + '/learning_curve_' + _id + '.png')


plt.close()

# train_scores_mean = np.mean(train_scores, axis=1)
# train_scores_std = np.std(train_scores, axis=1)
# test_scores_mean = np.mean(test_scores, axis=1)
# test_scores_std = np.std(test_scores, axis=1)


if np.isnan(train_sizes.tolist()).all():
#replace nan with -1
Expand All @@ -1158,26 +1171,31 @@ def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):


def plot_pca_2d(tmpdir,_id,features,target):
# import numpy as np
# import matplotlib.pyplot as plt


# from sklearn import decomposition
# import matplotlib.colors as mcolors
# from matplotlib.patches import Patch
"""Make PCA on 2D.

Parameters
----------
tmpdir: string
Temporary directory for saving 2d pca plot and json file
_id: string
Experiment ID in Aliro

features: np.darray/pd.DataFrame
Features in training dataset
target: np.darray/pd.DataFrame
Target in training dataset

# from sklearn import datasets

# np.random.seed(5)

# iris = datasets.load_iris()
# print(features)
Returns
-------
None
"""
X = np.array(features)
y = np.array(target)

print(set(y))






Expand All @@ -1188,59 +1206,23 @@ def plot_pca_2d(tmpdir,_id,features,target):
pca.fit(X)
X = pca.transform(X)

# plt.scatter(x,y, c = z, cmap = mcolors.ListedColormap(["black", "green"]))

# plt.show()


# version 1
# colors = np.array(["black", "green"])
# plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1, edgecolor='k')



# version 2
num_classes = len(set(y))
# generate the number of colors equal to the number of classes
colors = plt.cm.Set1(np.linspace(0, 1, num_classes))

plt.scatter(X[:, 0], X[:, 1], c=y, cmap=mcolors.ListedColormap(colors))
# plot the legend where the colors are mapped to the classes
plt.legend(handles=[Patch(color=colors[i], label="class_"+str(i)) for i in range(num_classes)])

# cb = plt.colorbar()
# loc = np.arange(0,max(label),max(label)/float(len(colors)))
# cb.set_ticks(loc)
# cb.set_ticklabels(colors)





# write x axis as pc1 and y axis as pc2
plt.xlabel('PC1')
plt.ylabel('PC2')




# print("X")
# print(X)


# ax.w_xaxis.set_ticklabels([])
# ax.w_yaxis.set_ticklabels([])
# ax.w_zaxis.set_ticklabels([])

# plt.show()
plt.savefig(tmpdir + _id + '/pca_' + _id + '.png')
plt.close()



path = tmpdir + _id + '/pcaJson_' + _id + '.json'
import json

plt.close()


# save X and y to json file
Expand All @@ -1249,17 +1231,11 @@ def plot_pca_2d(tmpdir,_id,features,target):
'y_pca': y.tolist()
}

# with open(tmpdir + _id + '/p-c-a-Json_' + _id + '.json', 'w') as f:
# json.dump(pca_dict, f)

# with open(tmpdir + _id + '/aaachoi_' + _id + '.json', 'w') as f:
# json.dump(pca_dict, f)

# save json file
save_json_fmt(outdir=tmpdir, _id=_id,
fname="pca-json.json", content=pca_dict)

#
# save pca_dict to json file with the path


def plot_pca_3d(tmpdir,_id,features,target):
Expand Down Expand Up @@ -1440,48 +1416,41 @@ def plot_pca_3d_iris(tmpdir,_id,features,target):
plt.savefig(tmpdir + _id + '/pca_' + _id + '.png')
plt.close()

def plot_tsne(tmpdir,_id,features,target):
def plot_tsne_2d(tmpdir,_id,features,target):

# import numpy as np
# import matplotlib.pyplot as plt
# from sklearn.manifold import TSNE

# X = np.array([[1, 1], [2, 1], [1, 0],
# [4, 7], [3, 5], [3, 6]])
# y = np.array([0, 0, 0, 1, 1, 1])
"""Make tsne on 2D.

# tsne = TSNE(n_components=2, random_state=0)
# X_2d = tsne.fit_transform(X)
Parameters
----------
tmpdir: string
Temporary directory for saving 2d t-sne plot and json file
_id: string
Experiment ID in Aliro

features: np.darray/pd.DataFrame
Features in training dataset
target: np.darray/pd.DataFrame
Target in training dataset

# plt.scatter(X_2d[:, 0], X_2d[:, 1])
# plt.show()


Returns
-------
None
"""

# X = np.array([[1, 1], [2, 1], [1, 0],
# [4, 7], [3, 5], [3, 6]])
# y = np.array([0, 0, 0, 1, 1, 1])

X = features
y = target

# print(X)
# print(y)

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

# df = pd.DataFrame()
# df["y"] = y
# df["comp-1"] = X_2d[:,0]
# df["comp-2"] = X_2d[:,1]

# version 2
num_classes = len(set(y))
num_classes = len(set(target))
# generate the number of colors equal to the number of classes
colors = plt.cm.Set1(np.linspace(0, 1, num_classes))

plt.scatter(X_2d[:,0], X_2d[:,1], c=y, cmap=mcolors.ListedColormap(colors))
plt.scatter(X_2d[:,0], X_2d[:,1], c=target, cmap=mcolors.ListedColormap(colors))
# plot the legend where the colors are mapped to the classes
plt.legend(handles=[Patch(color=colors[i], label="class_"+str(i)) for i in range(num_classes)])

Expand All @@ -1499,32 +1468,16 @@ def plot_tsne(tmpdir,_id,features,target):




# path = tmpdir + _id + '/tsneJson_' + _id + '.json'
import json



# save X and y to json file
tsne_dict = {
'X_tsne': X_2d.tolist(),
'y_tsne': y.tolist()
'y_tsne': target.tolist()
}

# print('tsne_dict',tsne_dict)

# with open(tmpdir + _id + '/t-sne-Json_' + _id + '.json', 'w') as f:
# json.dump(tsne_dict, f)
# with open(tmpdir + _id + '/wwwchoi_' + _id + '.json', 'w') as f:
# json.dump(tsne_dict, f)

save_json_fmt(outdir=tmpdir, _id=_id,
fname="tsne-json.json", content=tsne_dict)

# save_json_fmt(outdir=tmpdir, _id=_id,
# fname="value.json", content=metrics_dict)






Expand Down
2 changes: 1 addition & 1 deletion raspberrypi/productpage/css/style.css
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Expand Up @@ -480,7 +480,7 @@ to { opacity: 0; }
}


a#Downloadpage{
a#Downloadpage, a#installationpage{

color:#e3085d!important;
/* visibility: hidden; */
Expand Down
30 changes: 30 additions & 0 deletions raspberrypi/productpage/data/datasets/pmlb_small/iris/README.md
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@@ -0,0 +1,30 @@
# iris

## Summary Stats

#instances: 150

#features: 4

#binary_features: 0

#integer_features: 0

#float_features: 4

Endpoint type: integer

#Classes: 3

Imbalance metric: 0.0

## Feature Types

sepal-length:continous

sepal-width:continous

petal-length:continous

petal-width:continous

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