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### ----------------------------- ###
### libraries ###
### ----------------------------- ###
import gradio as gr
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from reader import get_article
### ------------------------------ ###
### data transformation ###
### ------------------------------ ###
# load dataset
uncleaned_data = pd.read_csv('data.csv')
# remove timestamp from dataset (always first column)
uncleaned_data = uncleaned_data.iloc[: , 1:]
data = pd.DataFrame()
# keep track of which columns are categorical and what
# those columns' value mappings are
# structure: {colname1: {...}, colname2: {...} }
cat_value_dicts = {}
final_colname = uncleaned_data.columns[len(uncleaned_data.columns) - 1]
# for each column...
for (colname, colval) in uncleaned_data.iteritems():
# check if col is already a number; if so, add col directly
# to new dataframe and skip to next column
if isinstance(colval.values[0], (np.integer, float)):
data[colname] = uncleaned_data[colname].copy()
continue
# structure: {0: "lilac", 1: "blue", ...}
new_dict = {}
val = 0 # first index per column
transformed_col_vals = [] # new numeric datapoints
# if not, for each item in that column...
for (row, item) in enumerate(colval.values):
# if item is not in this col's dict...
if item not in new_dict:
new_dict[item] = val
val += 1
# then add numerical value to transformed dataframe
transformed_col_vals.append(new_dict[item])
# reverse dictionary only for final col (0, 1) => (vals)
if colname == final_colname:
new_dict = {value : key for (key, value) in new_dict.items()}
cat_value_dicts[colname] = new_dict
data[colname] = transformed_col_vals
### -------------------------------- ###
### model training ###
### -------------------------------- ###
# select features and predicton; automatically selects last column as prediction
cols = len(data.columns)
num_features = cols - 1
x = data.iloc[: , :num_features]
y = data.iloc[: , num_features:]
# split data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
# instantiate the model (using default parameters)
model = LogisticRegression()
model.fit(x_train, y_train.values.ravel())
y_pred = model.predict(x_test)
### -------------------------------- ###
### article generation ###
### -------------------------------- ###
# borrow file reading function from reader.py
def get_feat():
feats = [abs(x) for x in model.coef_[0]]
max_val = max(feats)
idx = feats.index(max_val)
return data.columns[idx]
acc = str(round(metrics.accuracy_score(y_test, y_pred) * 100, 1)) + '%**'
most_imp_feat = get_feat() + "**"
info = get_article(acc, most_imp_feat)
### ------------------------------- ###
### interface creation ###
### ------------------------------- ###
# predictor for generic number of features
def general_predictor(*args):
features = []
# transform categorical input
for colname, arg in zip(data.columns, args):
if (colname in cat_value_dicts):
features.append(cat_value_dicts[colname][arg])
else:
features.append(arg)
# predict single datapoint
new_input = [features]
result = model.predict(new_input)
return cat_value_dicts[final_colname][result[0]]
# add data labels to replace those lost via star-args
inputls = []
for colname in data.columns:
# skip last column
if colname == final_colname:
continue
# access categories dict if data is categorical
# otherwise, just use a number input
if colname in cat_value_dicts:
radio_options = list(cat_value_dicts[colname].keys())
inputls.append(gr.inputs.Radio(choices=radio_options, type="value", label=colname))
else:
# add numerical input
inputls.append(gr.inputs.Number(label=colname))
# generate gradio interface
interface = gr.Interface(general_predictor, inputs=inputls, outputs="text", article=info['article'], css=info['css'], theme="grass", title=info['title'], allow_flagging='never', description=info['description'])
# show the interface
interface.launch() |