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autoViz.py
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autoViz.py
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import re
import pandas as pd
import plotly.graph_objects as go
from plotly.offline import plot
from plotly.colors import n_colors
import numpy as np
class autoViz:
"""This class implements model visualization.
Parameters
----------
preprocess_dict : dict, default = None
1st output result (DICT_PREPROCESS) of autoPipe module.
report : df, default = None
4th output result (dyna_report) of autoPipe module.
Example
-------
.. []:
References
----------
"""
def __init__(self,preprocess_dict = None,report = None ):
self.DICT_PREPROCESSING = preprocess_dict
self.dyna_report = report
def table_report(self):
"""This function implements heatmap style pipeline cluster's model evaluation report.
Parameters
----------
Example
-------
.. []
References
----------
"""
colors = n_colors('rgb(255, 200, 200)', 'rgb(200, 0, 0)', 9, colortype='rgb')
df = self.dyna_report
fig = go.Figure(data=[go.Table(
header=dict(values=list(df.columns),
fill_color='paleturquoise',
align='left'),
cells=dict(values=[df.Dataset,df.Model_Name,df.Best_Parameters,df.Accuracy,df.Precision,df.Recall,df.Latency],
# fill_color='lavender',
fill_color=[np.array(colors)[df.Accuracy],np.array(colors)[df.Precision], np.array(colors)[df.Recall]],
align='left'))
])
fig.update_layout(title = f'Pipeline Cluster Model Evaluation Report - autoViz <a href="https://www.linkedin.com/in/lei-tony-dong/"> ©Tony Dong</a>', font_size=8)
plot(fig)
fig.show()
def clf_model_retrieval(self,metrics = None):
"""This function implements classification model retrieval visualization.
Parameters
----------
metrics : str, default = None
Value in ["accuracy","precision","recall"].
Example
-------
.. [] https://Optimal-Flow.readthedocs.io/en/latest/demos.html#pipeline-cluster-traversal-experiments-model-retrieval-diagram-using-autoviz
References
----------
"""
columns = ["Dataset","Encode_low_dimension","Encode_high_dimension","Winsorize","Scale"]
df_pp = pd.DataFrame(columns=columns)
for i in list(self.DICT_PREPROCESSING.keys()):
row_pp = [i]
s = self.DICT_PREPROCESSING[i]
ext = re.search("Encoded Features:(.*)']", s).group(1)
if ("onehot_" in ext) and ("Frequency_" in ext):
row_pp.append('Low Dim_Onehot')
row_pp.append('High Dim_Frequency')
row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))
row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))
df_pp.loc[len(df_pp)] = row_pp
elif ("onehot_" in ext) and ("Mean_" in ext):
row_pp.append('Low Dim_Onehot')
row_pp.append('High Dim_Mean')
row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))
row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))
df_pp.loc[len(df_pp)] = row_pp
elif ("onehot_" in ext) and ("Mean_" not in ext) and ("Frequency_" not in ext):
row_pp.append('Low Dim_Onehot')
row_pp.append('High Dim_No Encoder')
row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))
row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))
df_pp.loc[len(df_pp)] = row_pp
elif ("Label_" in ext) and ("Frequency_" in ext):
row_pp.append('Low Dim_Label')
row_pp.append('High Dim_Frequency')
row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))
row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))
df_pp.loc[len(df_pp)] = row_pp
elif ("Label_" in ext) and ("Mean_" in ext):
row_pp.append('Low Dim_Label')
row_pp.append('High Dim_Mean')
row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))
row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))
df_pp.loc[len(df_pp)] = row_pp
elif ("Label_" in ext) and ("Mean_" not in ext) and ("Frequency_" not in ext):
row_pp.append('Low Dim_Label')
row_pp.append('High Dim_No Encoder')
row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))
row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))
df_pp.loc[len(df_pp)] = row_pp
elif ("Frequency_" in ext) and ("onehot_" not in ext) and ("Label_" not in ext):
row_pp.append('Low Dim_No Encoder')
row_pp.append('High Dim_Frequency')
row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))
row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))
df_pp.loc[len(df_pp)] = row_pp
elif ("Mean_" in ext) and ("onehot_" not in ext) and ("Label_" not in ext):
row_pp.append('Low Dim_No Encoder')
row_pp.append('High Dim_Mean')
row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))
row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))
df_pp.loc[len(df_pp)] = row_pp
elif ("Frequency_" not in ext) and ("Mean_" not in ext) and ("onehot_" not in ext) and ("Label_" not in ext):
row_pp.append('Low Dim_No Encoder')
row_pp.append('High Dim_No Encoder')
row_pp.append(re.search('winsor_(.*)-Scaler', s).group(1))
row_pp.append(re.search('-Scaler_(.*)-- ', s).group(1))
df_pp.loc[len(df_pp)] = row_pp
if metrics == "accuracy":
df_report_Accuracy = df_pp.merge(self.dyna_report[['Dataset','Accuracy']], how = 'left', on = 'Dataset')
bins = [0, 0.70, 0.90, 1]
labels = ["Low Accuracy","High Accuracy","Top Accuracy"]
df_report_Accuracy['Level'] = pd.cut(df_report_Accuracy['Accuracy'], bins=bins, labels=labels)
df_report_Accuracy['cnt'] = 1
df_report_Accuracy.loc[df_report_Accuracy['Scale'] == 'None','Scale'] = "No Scaler"
df_report_Accuracy['Scale'] = 'Scale_'+df_report_Accuracy['Scale']
df_report_Accuracy['Winsorize'] = 'Winsorize_' + df_report_Accuracy['Winsorize']
step1_df = df_report_Accuracy.groupby(['Encode_low_dimension','Dataset'], as_index=False)['cnt'].count().rename({"cnt":"Total","Dataset":"antecedentIndex","Encode_low_dimension":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step2_df = df_report_Accuracy.groupby(['Encode_low_dimension','Encode_high_dimension'], as_index=False)['cnt'].count().rename({"cnt":"Total","Encode_low_dimension":"antecedentIndex","Encode_high_dimension":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step3_df = df_report_Accuracy.groupby(['Encode_high_dimension','Winsorize'], as_index=False)['cnt'].count().rename({"cnt":"Total","Encode_high_dimension":"antecedentIndex","Winsorize":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step4_df = df_report_Accuracy.groupby(['Winsorize','Scale'], as_index=False)['cnt'].count().rename({"cnt":"Total","Winsorize":"antecedentIndex","Scale":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step5_df = df_report_Accuracy.groupby(['Scale','Level'], as_index=False)['cnt'].count().rename({"cnt":"Total","Scale":"antecedentIndex","Level":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']].dropna()
integrated_df = pd.concat([step1_df,step2_df,step3_df,step4_df,step5_df],axis = 0)
label_df = pd.DataFrame(integrated_df['antecedentIndex'].append(integrated_df['consequentIndex']).drop_duplicates(),columns = {"label"})
label_df['Number'] = label_df.reset_index().index
label_list = list(label_df.label)
source_df = pd.DataFrame(integrated_df['antecedentIndex'])
source_df = source_df.merge(label_df, left_on=['antecedentIndex'], right_on = ['label'],how = 'left')
source_list = list(source_df['Number'])
target_df = pd.DataFrame(integrated_df['consequentIndex'])
target_df = target_df.merge(label_df, left_on=['consequentIndex'], right_on = ['label'],how = 'left')
target_list = list(target_df['Number'])
value_list = [int(i) for i in list(integrated_df.Total)]
fig = go.Figure(data=[go.Sankey(
node = dict(
pad = 15,
thickness = 10,
line = dict(color = 'rgb(25,100,90)', width = 0.5),
label = label_list,
color = 'rgb(71,172,55)'
),
link = dict(
source = source_list,
target = target_list,
value = value_list
))])
fig.update_layout(title = f'Pipeline Cluster Traversal Experiments - autoViz {metrics} Retrieval Diagram <a href="https://www.linkedin.com/in/lei-tony-dong/"> ©Tony Dong</a>', font_size=8)
plot(fig)
fig.show()
elif metrics == "precision":
df_report_Precision = df_pp.merge(self.dyna_report[['Dataset','Precision']], how = 'left', on = 'Dataset')
bins = [0, 0.70, 0.90, 1]
labels = ["Low Precision","High Precision","Top Precision"]
df_report_Precision['Level'] = pd.cut(df_report_Precision['Precision'], bins=bins, labels=labels)
df_report_Precision['cnt'] = 1
df_report_Precision.loc[df_report_Precision['Scale'] == 'None','Scale'] = "No Scaler"
df_report_Precision['Scale'] = 'Scale_'+df_report_Precision['Scale']
df_report_Precision['Winsorize'] = 'Winsorize_' + df_report_Precision['Winsorize']
step1_df = df_report_Precision.groupby(['Encode_low_dimension','Dataset'], as_index=False)['cnt'].count().rename({"cnt":"Total","Dataset":"antecedentIndex","Encode_low_dimension":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step2_df = df_report_Precision.groupby(['Encode_low_dimension','Encode_high_dimension'], as_index=False)['cnt'].count().rename({"cnt":"Total","Encode_low_dimension":"antecedentIndex","Encode_high_dimension":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step3_df = df_report_Precision.groupby(['Encode_high_dimension','Winsorize'], as_index=False)['cnt'].count().rename({"cnt":"Total","Encode_high_dimension":"antecedentIndex","Winsorize":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step4_df = df_report_Precision.groupby(['Winsorize','Scale'], as_index=False)['cnt'].count().rename({"cnt":"Total","Winsorize":"antecedentIndex","Scale":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step5_df = df_report_Precision.groupby(['Scale','Level'], as_index=False)['cnt'].count().rename({"cnt":"Total","Scale":"antecedentIndex","Level":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']].dropna()
integrated_df = pd.concat([step1_df,step2_df,step3_df,step4_df,step5_df],axis = 0)
label_df = pd.DataFrame(integrated_df['antecedentIndex'].append(integrated_df['consequentIndex']).drop_duplicates(),columns = {"label"})
label_df['Number'] = label_df.reset_index().index
label_list = list(label_df.label)
source_df = pd.DataFrame(integrated_df['antecedentIndex'])
source_df = source_df.merge(label_df, left_on=['antecedentIndex'], right_on = ['label'],how = 'left')
source_list = list(source_df['Number'])
target_df = pd.DataFrame(integrated_df['consequentIndex'])
target_df = target_df.merge(label_df, left_on=['consequentIndex'], right_on = ['label'],how = 'left')
target_list = list(target_df['Number'])
value_list = [int(i) for i in list(integrated_df.Total)]
fig = go.Figure(data=[go.Sankey(
node = dict(
pad = 15,
thickness = 10,
line = dict(color = 'rgb(25,100,90)', width = 0.5),
label = label_list,
color = 'rgb(71,172,55)'
),
link = dict(
source = source_list,
target = target_list,
value = value_list
))])
fig.update_layout(title = f'Pipeline Cluster Traversal Experiments - autoViz {metrics} Retrieval Diagram <a href="https://www.linkedin.com/in/lei-tony-dong/"> ©Tony Dong</a>', font_size=8)
plot(fig)
fig.show()
elif metrics == "recall":
df_report_Recall = df_pp.merge(dyna_report[['Dataset','Recall']], how = 'left', on = 'Dataset')
bins = [0, 0.70, 0.90, 1]
labels = ["Low Recall","High Recall","Top Recall"]
df_report_Recall['Level'] = pd.cut(df_report_Recall['Recall'], bins=bins, labels=labels)
df_report_Recall['cnt'] = 1
df_report_Recall.loc[df_report_Recall['Scale'] == 'None','Scale'] = "No Scaler"
df_report_Recall['Scale'] = 'Scale_'+df_report_Recall['Scale']
df_report_Recall['Winsorize'] = 'Winsorize_' + df_report_Recall['Winsorize']
step1_df = df_report_Recall.groupby(['Encode_low_dimension','Dataset'], as_index=False)['cnt'].count().rename({"cnt":"Total","Dataset":"antecedentIndex","Encode_low_dimension":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step2_df = df_report_Recall.groupby(['Encode_low_dimension','Encode_high_dimension'], as_index=False)['cnt'].count().rename({"cnt":"Total","Encode_low_dimension":"antecedentIndex","Encode_high_dimension":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step3_df = df_report_Recall.groupby(['Encode_high_dimension','Winsorize'], as_index=False)['cnt'].count().rename({"cnt":"Total","Encode_high_dimension":"antecedentIndex","Winsorize":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step4_df = df_report_Recall.groupby(['Winsorize','Scale'], as_index=False)['cnt'].count().rename({"cnt":"Total","Winsorize":"antecedentIndex","Scale":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']]
step5_df = df_report_Recall.groupby(['Scale','Level'], as_index=False)['cnt'].count().rename({"cnt":"Total","Scale":"antecedentIndex","Level":"consequentIndex"},axis = 1)[['antecedentIndex','consequentIndex','Total']].dropna()
integrated_df = pd.concat([step1_df,step2_df,step3_df,step4_df,step5_df],axis = 0)
label_df = pd.DataFrame(integrated_df['antecedentIndex'].append(integrated_df['consequentIndex']).drop_duplicates(),columns = {"label"})
label_df['Number'] = label_df.reset_index().index
label_list = list(label_df.label)
source_df = pd.DataFrame(integrated_df['antecedentIndex'])
source_df = source_df.merge(label_df, left_on=['antecedentIndex'], right_on = ['label'],how = 'left')
source_list = list(source_df['Number'])
target_df = pd.DataFrame(integrated_df['consequentIndex'])
target_df = target_df.merge(label_df, left_on=['consequentIndex'], right_on = ['label'],how = 'left')
target_list = list(target_df['Number'])
value_list = [int(i) for i in list(integrated_df.Total)]
fig = go.Figure(data=[go.Sankey(
node = dict(
pad = 15,
thickness = 10,
line = dict(color = 'rgb(25,100,90)', width = 0.5),
label = label_list,
color = 'rgb(71,172,55)'
),
link = dict(
source = source_list,
target = target_list,
value = value_list
))])
fig.update_layout(title = f'Pipeline Cluster Traversal Experiments - autoViz {metrics} Retrieval Diagram <a href="https://www.linkedin.com/in/lei-tony-dong/"> ©Tony Dong</a>', font_size=8)
plot(fig)
fig.show()