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Validation process
"""
import sys
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import mlflow
from matplotlib import rcParams
from tableone import TableOne
# Set-up figures
rcParams['figure.figsize'] = 20, 5
rcParams['axes.spines.top'] = False
rcParams['axes.spines.right'] = False
def plot_cluster_size(df, data_type):
"""
Produce a bar plot of cluster size
--------
:param df: dataframe to plot
:param data_type: type of data - train, test, val, rec, sup
"""
# Number of patients
fig, ax = plt.subplots()
df.groupby('cluster').size().plot(ax=ax, kind='barh')
title = "Patient Cohorts"
ax.set_title(title)
ax.set_xlabel("Number of Patients", size=20)
ax.set_ylabel("Cluster")
plt.tight_layout()
mlflow.log_figure(fig, 'fig/' + title.replace(' ', '_') + '_' + data_type + '.png')
def plot_feature_hist(df, col, data_type):
"""
Produce a histogram plot for a chosen feature
--------
:param df: dataframe to plot
:param col: feature column to plot
:param data_type: type of data - train, test, val, rec, sup
"""
fig, ax = plt.subplots()
df.groupby('cluster')[col].plot(ax=ax, kind='hist', alpha=0.5)
ax.set_xlabel(col)
title = col + ' Histogram'
ax.set_title(title, size=20)
ax.legend()
plt.tight_layout()
mlflow.log_figure(fig, 'fig/' + title.replace(' ', '_') + '_' + data_type + '.png')
def plot_feature_bar(data, col, typ, data_type):
"""
Produce a bar plot for a chosen feature
--------
:param df: dataframe to plot
:param col: feature column to plot
:param typ: 'count' or 'percentage'
:param data_type: type of data - train, test, val, rec, sup
"""
if typ == 'count':
to_plot = data.groupby(['cluster']).apply(
lambda x: x.groupby(col).size())
x_label = "Number"
else:
to_plot = data.groupby(['cluster']).apply(
lambda x: 100 * x.groupby(col).size() / len(x))
x_label = "Percentage"
fig, ax = plt.subplots()
to_plot.plot(ax=ax, kind='barh')
title = "Patient Cohorts"
ax.set_title(title, size=20)
ax.set_xlabel(x_label + " of patients")
ax.set_ylabel("Cluster")
plt.tight_layout()
mlflow.log_figure(fig, 'fig/' + '_'.join((title.replace(' ', '_'), col, data_type + '.png')))
def plot_cluster_bar(data, typ, data_type):
"""
Produce a bar plot for a chosen feature
--------
:param data: data to plot
:param typ: 'count' or 'percentage'
:param data_type: type of data - train, test, val, rec, sup
"""
fig, ax = plt.subplots()
data.plot(ax=ax, kind='bar')
ax.set_title(typ, size=20)
ax.set_xlabel("Cluster")
ax.set_ylabel("Percentage")
ax.set_ylim(0, 100)
plt.tight_layout()
mlflow.log_figure(fig, 'fig/' + typ + '_' + data_type + '.png')
def plot_events(df, data_type):
"""
Plot events in the next 12 months based on metric table
--------
:param df: metric table
:param data_type: type of data - train, test, val, rec, sup
"""
df = df.drop('SafeHavenID', axis=1).set_index('cluster')
events = df.groupby('cluster').apply(lambda x: 100 * x.apply(
lambda x: len(x[x == 1]) / len(x)))
plot_cluster_bar(events, 'events', data_type)
def process_deceased_metrics(col):
"""
Process deceased column for plotting
-------
:param col: column to process
"""
n_deceased = 100 * ((col[col < '12+']).count()) / len(col)
res = pd.DataFrame({'alive': [100 - n_deceased], 'deceased': [n_deceased]})
return res
def plot_deceased(df, data_type):
"""
Plot events in the next 12 months based on metric table
--------
:param df: metric table
:param data_type: type of data - train, test, val, rec, sup
"""
survival = df.groupby('cluster')['time_to_death'].apply(
process_deceased_metrics).reset_index().drop(
'level_1', axis=1).set_index('cluster')
plot_cluster_bar(survival, 'survival', data_type)
def plot_therapies(df_year, results, data_type):
"""
Plot patient therapies per cluster
--------
:param df_year: unscaled data for current year
:param results: cluster results and safehaven id
:param data_type: type of data - train, test, val, rec, sup
"""
# Inhaler data for training group
therapies = df_year[['SafeHavenID', 'single_inhaler', 'double_inhaler', 'triple_inhaler']]
res_therapies = pd.merge(therapies, results, on='SafeHavenID', how='inner')
# Find counts/percentage per cluster
inhaler_cols = ['single_inhaler', 'double_inhaler', 'triple_inhaler']
inhals = res_therapies[['cluster'] + inhaler_cols].set_index('cluster')
in_res = inhals.groupby('cluster').apply(
lambda x: x.apply(lambda x: 100 * (x[x > 0].count()) / len(x)))
# Number of people without an inhaler presc
no_in = res_therapies.groupby('cluster').apply(
lambda x: 100 * len(x[(x[inhaler_cols] == 0).all(axis=1)]) / len(x)).values
# Rename columns for plotting
in_res.columns = [c[0] for c in in_res.columns.str.split('_')]
# Add those with no inhaler
in_res['no_inhaler'] = no_in
plot_cluster_bar(in_res, 'therapies', data_type)
def main():
# Load in config items
with open('../../../config.json') as json_config_file:
config = json.load(json_config_file)
data_path = config['model_data_path']
# Get datatype from cmd line
data_type = sys.argv[1]
run_name = sys.argv[2]
run_id = sys.argv[3]
# Set MLFlow parameters
model_type = 'hierarchical'
experiment_name = 'Model E - Date Specific: ' + model_type
mlflow.set_tracking_uri('http://127.0.0.1:5000/')
mlflow.set_experiment(experiment_name)
mlflow.start_run(run_id=run_id)
# Read in unscaled data, results and column names used to train model
columns = np.load(data_path + run_name + '_cols.npy', allow_pickle=True)
df_clusters = pd.read_pickle(data_path + "_".join((run_name, data_type, 'clusters.pkl')))
df_reduced = df_clusters[list(columns) + ['cluster']]
# Number of patients
plot_cluster_size(df_reduced, data_type)
# Generate mean/std table
t1_year = TableOne(df_reduced, categorical=[], groupby='cluster', pval=True)
t1yr_file = data_path + 't1_year_' + run_name + '_' + data_type + '.html'
t1_year.to_html(t1yr_file)
mlflow.log_artifact(t1yr_file)
# Histogram feature plots
plot_feature_hist(df_clusters, 'age', data_type)
plot_feature_hist(df_clusters, 'albumin_med_2yr', data_type)
# Bar plots
df_clusters['sex'] = df_clusters['sex_bin'].map({0: 'Male', 1: 'Female'})
plot_feature_bar(df_clusters, 'sex', 'percent', data_type)
plot_feature_bar(df_clusters, 'simd_decile', 'precent', data_type)
# Metrics for following 12 months
df_events = pd.read_pickle(data_path + 'metric_table_events.pkl')
df_counts = pd.read_pickle(data_path + 'metric_table_counts.pkl')
df_next = pd.read_pickle(data_path + 'metric_table_next.pkl')
# Merge cluster number with SafeHavenID and metrics
clusters = df_clusters[['SafeHavenID', 'cluster']]
df_events = clusters.merge(df_events, on='SafeHavenID', how='left').fillna(0)
df_counts = clusters.merge(df_counts, on='SafeHavenID', how='left').fillna(0)
df_next = clusters.merge(df_next, on='SafeHavenID', how='left').fillna('12+')
# Generate TableOne for events
cat_cols = df_events.columns[2:]
df_events[cat_cols] = df_events[cat_cols].astype('int')
event_limit = dict(zip(cat_cols, 5 * [1]))
event_order = dict(zip(cat_cols, 5 * [[1, 0]]))
t1_events = TableOne(df_events[df_events.columns[1:]], groupby='cluster',
limit=event_limit, order=event_order)
t1_events_file = data_path + '_'.join(('t1', data_type, 'events', run_name + '.html'))
t1_events.to_html(t1_events_file)
mlflow.log_artifact(t1_events_file)
# Generate TableOne for event counts
count_cols = df_counts.columns[2:]
df_counts[count_cols] = df_counts[count_cols].astype('int')
t1_counts = TableOne(df_counts[df_counts.columns[1:]], categorical=[], groupby='cluster')
t1_counts_file = data_path + '_'.join(('t1', data_type, 'counts', run_name + '.html'))
t1_counts.to_html(t1_counts_file)
mlflow.log_artifact(t1_counts_file)
# Generate TableOne for time to next events
next_cols = df_next.columns[2:]
next_event_order = dict(zip(next_cols, 5 * [['1', '3', '6', '12', '12+']]))
t1_next = TableOne(df_next[df_next.columns[1:]], groupby='cluster',
order=next_event_order)
t1_next_file = data_path + '_'.join(('t1', data_type, 'next', run_name + '.html'))
t1_next.to_html(t1_next_file)
mlflow.log_artifact(t1_next_file)
# Plot metrics
plot_events(df_events, data_type)
plot_deceased(df_next, data_type)
plot_therapies(df_clusters, clusters, data_type)
# Stop ML Flow
mlflow.end_run()
main()
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