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utils.py
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import importlib.util
import streamlit as st
import numpy as np
import pandas as pd
from sklearn.metrics import r2_score
from sklearn.metrics import accuracy_score
from sklearn.metrics import silhouette_score
from sklearn.metrics import r2_score
######for GP
import seaborn as sns
import matplotlib.pyplot as plt
import collections
import os
# utilities
import base64
import pickle
import uuid
import re
import scienceplots
# ============ import model from file =======================
def import_from_file(module_name: str, filepath: str):
"""
Imports a module from file.
Args:
module_name (str): Assigned to the module's __name__ parameter (does not
influence how the module is named outside of this function)
filepath (str): Path to the .py file
Returns:
The module
"""
spec = importlib.util.spec_from_file_location(module_name, filepath)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
# =============== model selected =======================
def model_platform(model_path):
template_dict = collections.defaultdict(dict)
template_dirs = [f for f in os.scandir(model_path) if f.is_dir() and f.name != "example"]
# Todo: Find a good way to sort templates, e.g. vy prepending a number to their name
# (e.g. 1_Image classification_PyTorch).
template_dirs = sorted(template_dirs, key = lambda e:e.name)
for template_dir in template_dirs:
try:
# st.write(template_dir)
# Template with task + framework.
task, framework = template_dir.name.split("_")
template_dict[task][framework] = template_dir.path
except ValueError:
# Templates with task only.
template_dict[template_dir.name] = template_dir.path
task = st.selectbox("model", list(template_dict.keys()))
if isinstance(template_dict[task], dict):
framework = st.selectbox('platform', list(template_dict[task].keys()))
template_dir = template_dict[task][framework]
else:
template_dir = template_dict[task]
# 使用import_from_file函数导入template_dir路径下的sidebar.py模块，并将其赋值给template_sidebar变量
template_alg = import_from_file(
"template_alg", os.path.join(template_dir, "alg.py")
)
return template_alg
# =============== some tools =======================
def create_data_with_group_and_counts(feature_type):
comp_ids = []
comp_counts = []
for name,group in feature_type:
comp_ids.append(name)
comp_counts.append(len(group))
comp_instances= list(zip(comp_ids,comp_counts))
comp_data = pd.DataFrame(comp_instances,columns=['ID','Count']).sort_values(by='Count')
return comp_data
# ============== check string in DataFrame ============
def check_string_NaN(df):
# check NaN
null_columns = df.columns[df.isnull().any()]
if len(null_columns) > 0:
st.error(f"Error: NaN in column {list(null_columns)} !")
st.stop()
# check string
string_columns = df.select_dtypes(include=[object]).columns
string_contains_columns = []
for column in string_columns:
if df[column].astype(str).str.contains('').any():
string_contains_columns.append(column)
if len(string_contains_columns) > 0:
st.error(f"Error: string in column {string_contains_columns} !")
st.stop()
# =============== dwonload button =======================
def download_button(object_to_download, download_filename, button_text, pickle_it=False):
if pickle_it:
try:
object_to_download = pickle.dumps(object_to_download)
except pickle.PicklingError as e:
st.write(e)
return None
else:
if isinstance(object_to_download, bytes):
pass
elif isinstance(object_to_download, pd.DataFrame):
object_to_download = object_to_download.to_csv(index=False)
# Try JSON encode for everything else
else:
object_to_download = pickle.dumps(object_to_download)
try:
# some strings <-> bytes conversions necessary here
b64 = base64.b64encode(object_to_download.encode()).decode()
except AttributeError as e:
b64 = base64.b64encode(object_to_download).decode()
button_uuid = str(uuid.uuid4()).replace('-', '')
button_id = re.sub('\d+', '', button_uuid)
prim_color = '#ffffff'
bg_color1 = '#66CDAA'
bg_color2 = '#66CDAA'
sbg_color = '#111111'
txt_color = '#111111'
font = 'Microsoft YaHei'
custom_css = f"""
<style>
#{button_id} {{
background-color: {bg_color1};
color: {txt_color};
padding: 0.25rem 0.75rem;
position: relative;
line-height: 1.6;
border-radius: 0.25rem;
border-width: 1px;
border-style: solid;
border-color: #ffffff;
border-image: initial;
filter: brightness(105%);
justify-content: center;
margin: 0px;
width: auto;
appearance: button;
display: inline-flex;
family-font: {font};
font-weight: 400;
letter-spacing: normal;
word-spacing: normal;
text-align: center;
text-rendering: auto;
text-transform: none;
text-indent: 0px;
text-shadow: none;
text-decoration: none;
}}
#{button_id}:hover {{
border-color: {prim_color};
color: #ffff00;
background-color: {bg_color2};
}}
#{button_id}:active {{
box-shadow: none;
background-color: #ff0066;
color: {sbg_color};
}}
</style> """
dl_link = custom_css + f'<a download="{download_filename}" class= "" id="{button_id}" ' \
f'href="data:file/txt;base64,{b64}">{button_text}</a><br></br>'
return dl_link
# =============== class =======================
class customPlot:
def __init__(self) -> None:
self.fig_title = None
self.fig_label = None
self.fig_legend = None
self.fontsize_option = ('MEDIUM','SMALL','BIGGER','SSMALL','SSSMALL')
self.fontsize_dict = {'SSSMALL':3,'SSMALL':5,'SMALL':8, 'MEDIUM':12, 'BIGGER':17}
self.color_option = ('MidnightBlue',
'Pink',
'Crimson',
'LavenderBlush',
'PaleVioletRed',
'HotPink',
'DeepPink',
'MediumVioletRed',
'Orchid',
'Thistle',
'plum',
'Violet',
'Magenta',
'Fuchsia',
'DarkMagenta',
'Purple',
'MediumOrchid',
'DarkVoilet',
'DarkOrchid',
'Indigo',
'BlueViolet',
'MediumPurple',
'MediumSlateBlue',
'SlateBlue',
'DarkSlateBlue',
'Lavender',
'GhostWhite',
'Blue',
'MediumBlue',
'LightPink',
'DarkBlue',
'Navy',
'RoyalBlue',
'CornflowerBlue',
'LightSteelBlue',
'LightSlateGray',
'SlateGray',
'DoderBlue',
'AliceBlue',
'SteelBlue',
'LightSkyBlue',
'SkyBlue',
'DeepSkyBlue',
'LightBLue',
'PowDerBlue',
'CadetBlue',
'Azure',
'LightCyan',
'PaleTurquoise',
'Cyan',
'Aqua',
'DarkTurquoise',
'DarkSlateGray',
'DarkCyan',
'Teal',
'MediumTurquoise',
'LightSeaGreen',
'Turquoise',
'Auqamarin',
'MediumAquamarine',
'MediumSpringGreen',
'MintCream',
'SpringGreen',
'SeaGreen',
'Honeydew',
'LightGreen',
'PaleGreen',
'DarkSeaGreen',
'LimeGreen',
'Lime',
'ForestGreen',
'Green',
'DarkGreen',
'Chartreuse',
'LawnGreen',
'GreenYellow',
'OliveDrab',
'Beige',
'LightGoldenrodYellow',
'Ivory',
'LightYellow',
'Yellow',
'Olive',
'DarkKhaki',
'LemonChiffon',
'PaleGodenrod',
'Khaki',
'Gold',
'Cornislk',
'GoldEnrod',
'FloralWhite',
'OldLace',
'Wheat',
'Moccasin',
'Orange',
'PapayaWhip',
'BlanchedAlmond',
'NavajoWhite',
'AntiqueWhite',
'Tan',
'BrulyWood',
'Bisque',
'DarkOrange',
'Linen',
'Peru',
'PeachPuff',
'SandyBrown',
'Chocolate',
'SaddleBrown',
'SeaShell',
'Sienna',
'LightSalmon',
'Coral',
'OrangeRed',
'DarkSalmon',
'Tomato',
'MistyRose',
'Salmon',
'Snow',
'LightCoral',
'RosyBrown',
'IndianRed',
'Red',
'Brown',
'FireBrick',
'DarkRed',
'Maroon',
'White',
'WhiteSmoke',
'Gainsboro',
'LightGrey',
'Silver',
'DarkGray',
'Gray',
'DimGray',
'Black')
self.color_dict = {'MidnightBlue':'#191970',
'Pink':'#FFC0CB',
'Crimson':'#DC143C',
'LavenderBlush':'#FFF0F5',
'PaleVioletRed':'#DB7093',
'HotPink':'#FF69B4',
'DeepPink':'#FF1493',
'MediumVioletRed':'#C71585',
'Orchid':'#DA70D6',
'Thistle':'#D8BFD8',
'plum':'#DDA0DD',
'Violet':'#EE82EE',
'Magenta':'#FF00FF',
'Fuchsia':'#FF00FF',
'DarkMagenta':'#8B008B',
'Purple':'#800080',
'MediumOrchid':'#BA55D3',
'DarkVoilet':'#9400D3',
'DarkOrchid':'#9932CC',
'Indigo':'#4B0082',
'BlueViolet':'#8A2BE2',
'MediumPurple':'#9370DB',
'MediumSlateBlue':'#7B68EE',
'SlateBlue':'#6A5ACD',
'DarkSlateBlue':'#483D8B',
'Lavender':'#E6E6FA',
'GhostWhite':'#F8F8FF',
'Blue':'#0000FF',
'MediumBlue':'#0000CD',
'LightPink':'#FFB6C1',
'DarkBlue':'#00008B',
'Navy':'#000080',
'RoyalBlue':'#4169E1',
'CornflowerBlue':'#6495ED',
'LightSteelBlue':'#B0C4DE',
'LightSlateGray':'#778899',
'SlateGray':'#708090',
'DoderBlue':'#1E90FF',
'AliceBlue':'#F0F8FF',
'SteelBlue':'#4682B4',
'LightSkyBlue':'#87CEFA',
'SkyBlue':'#87CEEB',
'DeepSkyBlue':'#00BFFF',
'LightBLue':'#ADD8E6',
'PowDerBlue':'#B0E0E6',
'CadetBlue':'#5F9EA0',
'Azure':'#F0FFFF',
'LightCyan':'#E1FFFF',
'PaleTurquoise':'#AFEEEE',
'Cyan':'#00FFFF',
'Aqua':'#D4F2E7',
'DarkTurquoise':'#00CED1',
'DarkSlateGray':'#2F4F4F',
'DarkCyan':'#008B8B',
'Teal':'#008080',
'MediumTurquoise':'#48D1CC',
'LightSeaGreen':'#20B2AA',
'Turquoise':'#40E0D0',
'Auqamarin':'#7FFFAA',
'MediumAquamarine':'#00FA9A',
'MediumSpringGreen':'#00FF7F',
'MintCream':'#F5FFFA',
'SpringGreen':'#3CB371',
'SeaGreen':'#2E8B57',
'Honeydew':'#F0FFF0',
'LightGreen':'#90EE90',
'PaleGreen':'#98FB98',
'DarkSeaGreen':'#8FBC8F',
'LimeGreen':'#32CD32',
'Lime':'#00FF00',
'ForestGreen':'#228B22',
'Green':'#008000',
'DarkGreen':'#006400',
'Chartreuse':'#7FFF00',
'LawnGreen':'#7CFC00',
'GreenYellow':'#ADFF2F',
'OliveDrab':'#556B2F',
'Beige':'#F5F5DC',
'LightGoldenrodYellow':'#FAFAD2',
'Ivory':'#FFFFF0',
'LightYellow':'#FFFFE0',
'Yellow':'#FFFF00',
'Olive':'#808000',
'DarkKhaki':'#BDB76B',
'LemonChiffon':'#FFFACD',
'PaleGodenrod':'#EEE8AA',
'Khaki':'#F0E68C',
'Gold':'#FFD700',
'Cornislk':'#FFF8DC',
'GoldEnrod':'#DAA520',
'FloralWhite':'#FFFAF0',
'OldLace':'#FDF5E6',
'Wheat':'#F5DEB3',
'Moccasin':'#FFE4B5',
'Orange':'#FFA500',
'PapayaWhip':'#FFEFD5',
'BlanchedAlmond':'#FFEBCD',
'NavajoWhite':'#FFDEAD',
'AntiqueWhite':'#FAEBD7',
'Tan':'#D2B48C',
'BrulyWood':'#DEB887',
'Bisque':'#FFE4C4',
'DarkOrange':'#FF8C00',
'Linen':'#FAF0E6',
'Peru':'#CD853F',
'PeachPuff':'#FFDAB9',
'SandyBrown':'#F4A460',
'Chocolate':'#D2691E',
'SaddleBrown':'#8B4513',
'SeaShell':'#FFF5EE',
'Sienna':'#A0522D',
'LightSalmon':'#FFA07A',
'Coral':'#FF7F50',
'OrangeRed':'#FF4500',
'DarkSalmon':'#E9967A',
'Tomato':'#FF6347',
'MistyRose':'#FFE4E1',
'Salmon':'#FA8072',
'Snow':'#FFFAFA',
'LightCoral':'#F08080',
'RosyBrown':'#BC8F8F',
'IndianRed':'#CD5C5C',
'Red':'#FF0000',
'Brown':'#A52A2A',
'FireBrick':'#B22222',
'DarkRed':'#8B0000',
'Maroon':'#800000',
'White':'#FFFFFF',
'WhiteSmoke':'#F5F5F5',
'Gainsboro':'#DCDCDC',
'LightGrey':'#D3D3D3',
'Silver':'#C0C0C0',
'DarkGray':'#A9A9A9',
'Gray':'#808080',
'DimGray':'#696969',
'Black':'#000000'}
def set_title_fontsize(self, key):
options = self.fontsize_option
options_selected = st.selectbox('title fontsize', options, key=key)
return options_selected
def set_label_fontsize(self, key):
options = self.fontsize_option
options_selected = st.selectbox('label fontsize', options, key=key)
return options_selected
def set_legend_fontsize(self, key):
options = self.fontsize_option
options_selected = st.selectbox('legend fontsize', options, key=key)
return options_selected
def set_tick_fontsize(self, key):
options = self.fontsize_option
options_selected = st.selectbox('tick fontsize', options, key=key)
return options_selected
def set_color(self, name, num, key):
# name: bin or line
options = self.color_option
options_selected = st.selectbox(name, options,num,key=key)
return options_selected
def map_fontsize_options(self, options_selected):
options = np.array([self.fontsize_dict[x] for x in options_selected[:4]])
plt.rc('axes', titlesize=options[0])
plt.rc('axes', labelsize=options[1])
plt.rc('xtick', labelsize=options[2])
plt.rc('ytick', labelsize=options[2])
plt.rc('legend', fontsize=options[3], frameon=False)
def map_color_options(self, options_selected):
color = [self.color_dict[x] for x in options_selected[4:]]
return color
def target_hist_kde(self, options_selected, target_name, target_value):
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig, ax = plt.subplots()
ax = sns.distplot(target_value.dropna(), bins=10, kde_kws={"color": color[0], "label": "KDE","alpha":1},
hist_kws={"color": color[1],"alpha":1})
plt.title(target_name + " Statistics ")
plt.legend()
# img_path = os.path.join(target_dir, target_name+' Statistics.png')
# fig.savefig(img_path)
st.pyplot(fig)
def feature_hist_kde(self, options_selected, feature_name, feature_value):
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig, ax = plt.subplots()
ax = sns.distplot(feature_value.dropna(), bins=10, kde_kws={"color": color[0], "label": "KDE","alpha":1},
hist_kws={"color": color[1],"alpha":1})
plt.title(feature_name + " Statistics ")
plt.legend()
# img_path = os.path.join(target_dir, feature_name+' Statistics.png')
# fig.savefig(img_path)
st.pyplot(fig)
def featureSets_statistics_hist(self, options_selected, IDs, Counts):
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig, ax = plt.subplots()
ax = plt.bar(IDs, Counts,color=color[0],label='Count')
plt.title(" Recipes Statistics ")
plt.xticks(IDs, rotation='vertical')
plt.xlabel("Recipes")
plt.ylabel("Frequency")
plt.tight_layout()
plt.legend()
# img_path = os.path.join(target_dir, 'Recipes Statistics.png')
# fig.savefig(img_path)
st.pyplot(fig)
def feature_distribution(self,options_selected,feature_name, feature_value):
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig, ax = plt.subplots()
plt.hist(x=feature_value, bins='auto', color=color,alpha=0.7, rwidth=0.85)
number = (feature_value != 0).values.sum()
occupy_ratio = int(number/len(feature_value) * 100)
number_string = str(number) + " data points"+'\n'+ str(occupy_ratio)+ "% of data"
plt.text(0.1, 0.8, number_string, transform=ax.transAxes, fontdict={'color': 'black'})
plt.title("Distribution of "+ feature_name)
plt.ylabel("Frequency")
plt.xlabel("Atomic Percentage (at.%)")
plt.legend()
# img_path = os.path.join(target_dir, feature_name +'Data Distribution.png')
# fig.savefig(img_path)
st.pyplot(fig)
def features_and_targets(self,options_selected, data, features, targets):
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig = sns.PairGrid(data, x_vars=features, y_vars=targets).map(sns.regplot,ci=None,scatter_kws={"color": color[0]}, line_kws={"color": color[1]})
plt.legend()
# img_path = os.path.join(target_dir, 'Features and Targets.png')
# fig.savefig(img_path)
st.pyplot(fig)
def targets_and_targets(self,options_selected, data, targets):
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig = sns.PairGrid(data, x_vars=targets, y_vars=targets, height=2, aspect=1.5).map(sns.regplot,ci=None,scatter_kws={"color": color[0]}, line_kws={"color": color[1]})
for i, j in zip(*np.triu_indices_from(fig.axes, 1)):
fig.axes[i, j].set_visible(False)
for i in range(len(targets)):
fig.axes[i, i].set_visible(False)
# plt.title(" Targets and Targets ")
plt.legend()
# img_path = os.path.join(target_dir, 'Targets and Targets.png')
# fig.savefig(img_path)
st.pyplot(fig)
def corr_feature_target(self, options_selected, corr):
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig, ax = plt.subplots()
ax = plt.barh(corr.index, corr.values, color=color)
plt.xlabel('Correlation')
plt.title('Feature Correlations')
plt.legend()
# img_path = os.path.join(target_dir, 'Feature Correlations.png')
# fig.savefig(img_path)
st.pyplot(fig)
def corr_feature_target_mir(self, options_selected, corr_mir):
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig, ax = plt.subplots()
ax = plt.barh(corr_mir.index, corr_mir['mutual info'], color=color)
plt.xlabel('Correlation')
plt.title('Feature Correlations')
plt.legend()
st.pyplot(fig)
def corr_cofficient(self, options_selected, is_mask, corr_matrix):
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
cmap = sns.diverging_palette(220, 10, as_cmap=True)
fig, ax = plt.subplots()
if is_mask == "Yes":
mask = np.zeros_like(corr_matrix, dtype=bool)
mask[np.triu_indices_from(mask)] = True
ax = sns.heatmap(corr_matrix, mask=mask, cmap=cmap, linewidths=0.5,square = True, annot=True)
plt.legend()
# img_path = os.path.join(target_dir, 'Correlation Matrix.png')
# fig.savefig(img_path)
st.pyplot(fig)
else:
ax = sns.heatmap(corr_matrix,cmap = cmap, linewidths=0.5,square = True, annot=True)
plt.legend()
# img_path = os.path.join(target_dir, 'Correlation Cofficient.png')
# fig.savefig(img_path)
st.pyplot(fig)
def feature_missing(self,options_selected, record_missing, missing_stats):
# Histogram of missing fraction in each features
if record_missing is None:
raise NotImplementedError("Missing values have not been calculated.")
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig, ax = plt.subplots()
ax = plt.hist(missing_stats['missing_fraction'], color=color[0], align='left')
plt.title(" Fraction of Missing Values Histogram ")
plt.xlabel("Missing Fraction")
plt.ylabel("Count of Features")
plt.tight_layout()
plt.legend()
# img_path = os.path.join(target_dir, 'Fraction of Missing Values Histogram.png')
# fig.savefig(img_path)
st.pyplot(fig)
def feature_nunique(self, options_selected, record_single_unique, unique_stats):
# Histogram of number of unique values in each feature
if record_single_unique is None:
raise NotImplementedError("Unique values have not been calculated.")
assert len(options_selected) >= 4, "options insufficient !"
self.map_fontsize_options(options_selected)
color = self.map_color_options(options_selected)
fig, ax = plt.subplots()
ax = plt.hist(unique_stats, color=color[0], align = "left")
plt.title('Number of Unique Values Histogram')
plt.xlabel('Unique Values')
plt.ylabel('Frequency')
plt.tight_layout()
plt.legend()
# img_path = os.path.join(target_dir, 'Number of Unique Values Histogram.png')
# fig.savefig(img_path)
st.pyplot(fig)
def feature_importance(self,record_zero_importance, feature_importances, plot_n = 15):
# Plots `plot_n` most important features and the cumulative importance of features.
# If `threshold` is provided, prints the number of features needed to reach `threshold`
# cumulative importance.
# Parameters
# ------
# plot_n: int, default = 15
# Number of moset important features to plot. Default to 15 or the maximum number of features
# whichever is smller
# threshold: float, between 0 and 1 default = None
# Threshold for printing information about cumulative importances
if record_zero_importance is None:
raise NotImplementedError("Unique values have not been calculated.")
# Need to adjust number of features if greater than the features in the data
if plot_n > feature_importances.shape[0]:
# plot_n = feature_importances.shape[0] - 1
plot_n = feature_importances.shape[0]
# assert len(options_selected) >= 4, "options insufficient !"
# self.map_fontsize_options(options_selected)
# color = self.map_color_options(options_selected)
with plt.style.context(['nature','no-latex']):
fig, ax = plt.subplots()
# Need to reverse the index to plot most importance on top
# There might be a more efficient mothod to accomplish this
ax.barh(list(reversed(list(feature_importances.index[:plot_n]))),
feature_importances['normalized_importance'][:plot_n],
align = 'center', edgecolor = 'k')
# Set the yticks and labels
ax.set_yticks(list(reversed(list(feature_importances.index[:plot_n]))))
ax.set_yticklabels(feature_importances['feature'][:plot_n])
plt.xlabel('Normalized Importance');
plt.title('Feature Importances')
plt.legend()
# img_path = os.path.join(target_dir, 'Number of Unique Values Histogram.png')
# fig.savefig(img_path)
st.pyplot(fig)
def pred_vs_actual(self,actual,pred):
with plt.style.context(['nature','no-latex']):
fig, ax = plt.subplots()
ax.scatter(actual, pred, marker='o', s=18, color='#000080',zorder=1, facecolors='none')
lims = [
np.min([ax.get_xlim(), ax.get_ylim()]), # min of both axes
np.max([ax.get_xlim(), ax.get_ylim()]), # max of both axes
]
ax.tick_params(direction='in', length=5)
ax.plot(lims, lims, 'k-', zorder=2, linewidth=2, linestyle='solid', color='#FF0000')
ax.set_xlim(lims)
ax.set_ylim(lims)
plt.xlabel("Actual")
plt.ylabel("Prediction")
# img_path = os.path.join(target_dir, 'prediction vs label.png')
# fig.savefig(img_path)
st.pyplot(fig)
def confusion_matrix(self,confusion_matrix):
cmap = sns.diverging_palette(220, 10, as_cmap=True)
fig, ax = plt.subplots()
ax = sns.heatmap(confusion_matrix, cmap=cmap, linewidths=0.5,square =True, annot=True)
plt.ylabel("Actual")
plt.xlabel("Predict")
# plt.legend()
# img_path = os.path.join(target_dir, 'prediction vs label.png')
# fig.savefig(img_path)
st.pyplot(fig)
class FeatureSelector:
def __init__(self, features, targets) -> None:
# origin features and targets
self.features = features
self.targets = targets
# model
self.model = None
if targets is None:
st.write('No targets provided !')
# origin features name
self.base_features = list(features.columns)
# one-hot features name
self.one_hot_features = None
# features_dropped_single + one-hot_features
# self.feature_all = None
# Dataframes recording information about features to remove
self.record_missing = None
self.record_single_unique = None
self.record_collinear = None
self.record_zero_importance = None
self.record_low_importance = None
# Dataframe recording the different features
self.features_dropped_single = None
self.features_plus_oneHot = None
self.dummy_features = None
self.features_dropped_f_t = None
self.features_dropped_collinear = None
self.features_dropped_low_importance = None
self.features_dropped_zero_importance = None
self.missing_stats = None
self.unique_stats = None
self.corr_matrix = None
self.feature_importances = None
# Dictionary to hold removal operations
self.ops = {}
self.one_hot_correlated = False
def identify_missing(self, missing_threshold):
# Find the features with a fraction of missing values above `missing_threshold`
self.missing_threshold = missing_threshold
# Calaulate the fraction of missing in each column
missing_series = self.features.isnull().sum() / self.features.shape[0]
self.missing_stats = pd.DataFrame(missing_series).rename(columns={'index': 'feature', 0: 'missing_fraction'})
# Sort with highest number of missing values on top
self.missing_stats = self.missing_stats.sort_values('missing_fraction', ascending = False)
# Find the columns with a missing percentage above the threshold
record_missing = pd.DataFrame(missing_series[missing_series > missing_threshold]).reset_index().rename(
columns={'index':'feature', 0: 'missing_fraction'})
to_drop = list(record_missing['feature'])
self.record_missing = record_missing
self.ops['missing'] = to_drop
def identify_nunique(self, counts=1):
# Find features with only a single unique value. NaN do not count as a unique value.
# Calculate the unique counts in each column
# unique value statistic times
self.counts = counts
unique_counts = self.features.nunique()
self.unique_stats = pd.DataFrame(unique_counts).rename(columns = {'index': 'feature', 0: 'nunique'})
self.unique_stats = self.unique_stats.sort_values('nunique', ascending = True)
# Find the columns with only one unique count
record_single_unique = pd.DataFrame(unique_counts[unique_counts <= self.counts]).reset_index().rename(
columns = {'index':'feature', 0: 'nunique'})
to_drop = list(record_single_unique['feature'])
self.record_single_unique = record_single_unique
self.ops['single_unique'] = to_drop
def one_hot_feature_encoder(self,one_hot=False):
# Whether to one-hot encode the features before calculating the correlation coefficients
# if use, need ensure the nuerial features have been processed by drop_single
# Calculate the correlation between every column
if one_hot:
# One hot encoding
self.dummy_features = pd.get_dummies(self.features)
self.one_hot_features = [column for column in self.dummy_features.columns if column not in self.base_features]
# Add one hot encoded data to original data
self.features_plus_oneHot = pd.concat([self.dummy_features[self.one_hot_features], self.features], axis=1)
def identify_collinear(self, corr_matrix, correlation_threshold):
# Finds collinear features based on the correlation coefficient between features.
# For each pair of features with a correlation coefficient greather than `correlation_threshold`,
# only one of the pair is identified for removal.
# Parameters
# --------
# correlation_threshold : float between 0 and 1
# Value of the Pearson correlation cofficient for identifying correlation features
self.correlation_threshold = correlation_threshold
# Extract the upper triangle of the correlation matrix
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
# Select the features withc correlations above the threshold
# Need to use the absolute value
to_drop = [column for column in upper.columns if any(upper[column].abs() > correlation_threshold)]
# Dataframe to hold correlated pairs
record_collinear = pd.DataFrame(columns = ['drop_feature', 'corr_feature','corr_value'])
# Iterate through the colummns to drop to record pairs of correlated features
for column in to_drop:
# Find the correlated features
corr_features = list(upper.index[upper[column].abs() > correlation_threshold])
# Find the correlated values
corr_values = list(upper[column][upper[column].abs() > correlation_threshold])
drop_features = [column for _ in range(len(corr_features))]
# Record the information (needa temp df for now)
temp_df = pd.DataFrame.from_dict({'drop_feature': drop_features,
'corr_feature': corr_features,
'corr_value':corr_values})
# Add to dataframe
record_collinear = record_collinear.append(temp_df, ignore_index=True)
self.record_collinear = record_collinear
def judge_drop_f_t_after_f_f(self, target_selected, corr_matrix):
# final judge the drop feature by the correlation with target
# check the selected target number
assert len(target_selected) == 1, 'please choose only one feature !'
# st.write(self.record_collinear.shape[0])
for row in range(self.record_collinear.shape[0]):
need_judge_feature_idx = list(self.record_collinear[['drop_feature','corr_feature']].iloc[row,:])
need_judge_feature_value = corr_matrix.loc[need_judge_feature_idx, target_selected]
feature_not_drop_index = list(need_judge_feature_value.abs().idxmax())
feature_need_drop_index = list(need_judge_feature_value.abs().idxmin())
self.record_collinear.loc[row,'corr_feature'] = pd.DataFrame(feature_not_drop_index).loc[0,0]
self.record_collinear.loc[row,'drop_feature'] = pd.DataFrame(feature_need_drop_index).loc[0,0]
self.ops['collinear'] = self.record_collinear['drop_feature']
# corr_threshold
def judge_drop_f_t(self, target_selected, corr_matrix, corr_threshold):
assert len(target_selected) == 1, 'please choose only one feature !'
f_t = corr_matrix.loc[target_selected]
less_than_thresh = f_t.abs().lt(corr_threshold)
cols_with_less_than_thresh = less_than_thresh.any()
self.ops['f_t_low_corr'] = cols_with_less_than_thresh[cols_with_less_than_thresh == True].index.tolist()
def identify_zero_low_importance(self, cumulative_importance=0.95):
# Sort features according to importance
feature_importances = self.feature_importances.sort_values('importance', ascending=False).reset_index(drop = True)
# st.write(feature_importances)
# Normalize the feature importance to add up to one
feature_importances['normalized_importance'] = feature_importances['importance'] / feature_importances['importance'].sum()
feature_importances['cumulative_importance'] = np.cumsum(feature_importances['normalized_importance'])
# st.write(feature_importances)
# st.write('---')
# Extract the features with zero importance
record_zero_importance = feature_importances[feature_importances['importance'] == 0.0]
to_drop = list(record_zero_importance['feature'])
self.feature_importances = feature_importances
self.record_zero_importance = record_zero_importance
self.ops['zero_importance'] = to_drop
st.write('\n%d features with zero importance.\n' % len(self.ops['zero_importance']))
self.cumulative_importance = cumulative_importance
# Make sure most importance features are on top
# self.feature_importances = self.feature_importances.sort_values('cumulative_importance')
# Identify the features not needed to reach the cumulative_importance
record_low_importance = self.feature_importances[self.feature_importances['cumulative_importance'] > cumulative_importance]
to_drop = list(record_low_importance['feature'])
self.record_low_importance = record_low_importance
self.ops['low_importance'] = to_drop
st.write('%d features required for cumulative importance of %0.2f .' % (len(self.feature_importances) -
len(self.record_low_importance), self.cumulative_importance))
# st.write('%d features do not contribute to cumulative importance of %0.2f.\n' % (len(self.ops['low_importance']),
# self.cumulative_importance))
def feature_importance_select_show(self):
st.write('---')
st.write(self.feature_importances)
tmp_download_link = download_button(self.feature_importances, f'特征重要性数据.csv', button_text='download')
st.markdown(tmp_download_link, unsafe_allow_html=True)
plot = customPlot()
plot.feature_importance(self.record_zero_importance, self.feature_importances, plot_n = 15)
self.features_dropped_zero_importance = self.features.drop(columns=self.ops['zero_importance'])
self.features_dropped_low_importance = self.features.drop(columns=self.ops['low_importance'])
with st.expander('处理之后的数据'):
col1, col2 = st.columns([1,1])
with col1:
st.write('dropped zero importance')
st.write(self.features_dropped_zero_importance)
tmp_download_link = download_button(self.features_dropped_zero_importance, f'特征重要性-丢弃0贡献特征.csv', button_text='download')
st.markdown(tmp_download_link, unsafe_allow_html=True)
with col2:
st.write('dropped low importance')
st.write(self.features_dropped_low_importance)
tmp_download_link = download_button(self.features_dropped_low_importance, f'特征重要性-丢弃低贡献特征.csv', button_text='download')
st.markdown(tmp_download_link, unsafe_allow_html=True)
def LinearRegressor(self):
# One hot encoding
features = pd.get_dummies(self.features)
# Extract feature names
feature_names = list(features.columns)
# Conbert to np array
features = np.array(self.features)
# Empty array for feature importances
feature_importance_values = np.zeros(len(feature_names))
self.model.fit(self.features, self.targets)
feature_importance_values = abs(self.model.coef_)
self.feature_importances = pd.DataFrame({'feature': feature_names,'importance':feature_importance_values})
def RidgeRegressor(self):
# One hot encoding
features = pd.get_dummies(self.features)
# Extract feature names
feature_names = list(features.columns)
# Conbert to np array
features = np.array(self.features)
# Empty array for feature importances
feature_importance_values = np.zeros(len(feature_names))
self.model.fit(self.features, self.targets)
feature_importance_values = abs(self.model.coef_)
self.feature_importances = pd.DataFrame({'feature': feature_names,'importance':feature_importance_values})
def LassoRegressor(self):
# One hot encoding
features = pd.get_dummies(self.features)
# Extract feature names
feature_names = list(features.columns)
# Conbert to np array
features = np.array(self.features)
# Empty array for feature importances
feature_importance_values = np.zeros(len(feature_names))