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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() |