def plot_cluster():
"""
Generate the Cluster plot as a PNG
Parameters
----------
job_id: str
x_axis: str
Name of column from user dataset to be used for the x axis of the plot
y_axis: str
Name of column from user dataset to be used for the y axis of the plot
Returns
-------
image/png
"""
job_id = request.args.get('job_id')
x_axis = request.args.get('x_axis')
y_axis = request.args.get('y_axis')
show_ticks = request.args.get('show_ticks', 'True') == 'True'
min_members = int(request.args.get('min_members', None))
plot_best = request.args.get('plot_best', 'True') == 'True'
if job_id is None or x_axis is None or y_axis is None:
return None
best_tasks = tasks_to_best_results(job_id, min_members)
viz_columns = [x_axis, y_axis]
data = job_to_data(job_id)
fig = plot_cluster_fig(data, viz_columns, best_tasks, show_ticks)
cluster_plot = fig_to_png(fig)
response = make_response(cluster_plot.getvalue())
response.mimetype = 'image/png'
return response
def plot_task():
"""
Generate the Cluster plot with visualization options for a single task.
Parameters
----------
job_id: str
task_id: str
x_axis: str - column from the dataset to be used for plotting
y_axis: str - column from the dataset to be used for plotting
show_ticks: boolean
Returns
-------
html
"""
job_id = request.args.get('job_id')
task_id = int(request.args.get('task_id'))
x_axis = request.args.get('x_axis')
y_axis = request.args.get('y_axis')
show_ticks = request.args.get('show_ticks', 'True') == 'True'
if job_id is None or task_id is None:
return None
data = job_to_data(job_id)
task = db.session.query(Task).filter_by(job_id=job_id,
task_id=task_id).first()
viz_columns = get_viz_columns(
db.session.query(Job).filter_by(job_id=job_id).first(), x_axis, y_axis)
fig = plot_single_cluster_fig(data, viz_columns, task.labels, task.bic,
task.k, show_ticks)
cluster_plot = fig_to_png(fig)
response = make_response(cluster_plot.getvalue())
response.mimetype = 'image/png'
return response
def plot_count():
"""
Generate the Count plot as a PNG
Parameters
----------
job_id: str
min_members: int, optional
Minimum number of members required in all clusters in an experiment to consider the experiment for the report.
Returns
-------
image/png
"""
job_id = request.args.get('job_id', None)
min_members = int(request.args.get('min_members', None))
if job_id is None:
return None
tasks = mongo_get_tasks(job_id)
if min_members is not None:
tasks = filter_by_min_members(tasks, min_members)
fig = plot_count_fig(tasks)
count_plot = fig_to_png(fig)
response = make_response(count_plot.getvalue())
response.mimetype = 'image/png'
return response
def plot_cluster():
"""
Generate the Cluster plot as a PNG
Parameters
----------
job_id: str
x_axis: str
Name of column from user dataset to be used for the x axis of the plot
y_axis: str
Name of column from user dataset to be used for the y axis of the plot
Returns
-------
image/png
"""
job_id = request.args.get('job_id')
x_axis = request.args.get('x_axis')
y_axis = request.args.get('y_axis')
show_ticks = request.args.get('show_ticks', 'True') == 'True'
min_members = int(request.args.get('min_members', None))
if job_id is None or x_axis is None or y_axis is None:
return None
job = mongo_get_job(job_id)
tasks = mongo_get_tasks(job_id)
if min_members is not None:
tasks = filter_by_min_members(tasks, min_members)
covar_types, covar_tieds, ks, labels, bics, task_ids = tasks_to_best_results(tasks)
s3_file_key = job['s3_file_key']
viz_columns = [x_axis, y_axis]
data = s3_to_df(s3_file_key)
fig = plot_cluster_fig(data, viz_columns, zip(covar_types, covar_tieds, labels, ks, bics), show_ticks)
cluster_plot = fig_to_png(fig)
response = make_response(cluster_plot.getvalue())
response.mimetype = 'image/png'
return response
def plot_correlation():
"""
Generate the Correlation heat map as a PNG
Parameters
----------
job_id: str
Returns
-------
image/png
"""
job_id = request.args.get('job_id')
if job_id is None:
return None
job = db.session.query(Job).filter_by(job_id=job_id).first()
s3_file_key = job.s3_file_key
data = s3_to_df(s3_file_key)
fig = plot_correlation_fig(data)
correlation_plot = fig_to_png(fig)
response = make_response(correlation_plot.getvalue())
response.mimetype = 'image/png'
return response
def plot_count():
"""
Generate the Count plot as a PNG
Parameters
----------
job_id: str
min_members: int, optional
Minimum number of members required in all clusters in an experiment to
consider the experiment for the report.
Returns
-------
image/png
"""
job_id = request.args.get('job_id', None)
if job_id is None:
return None
# TODO Compute min_members and save in the DB as a field.
fig = plot_count_fig(job_id)
count_plot = fig_to_png(fig)
response = make_response(count_plot.getvalue())
response.mimetype = 'image/png'
return response
def plot_aic_bic():
"""
Generate the AIC-BIC plot as a PNG
Parameters
----------
job_id: str
min_members: int, optional
Minimum number of members required in all clusters in an experiment to
consider the experiment for the report.
Returns
-------
image/png
"""
job_id = request.args.get('job_id', None)
if job_id is None:
return None
# TODO save min members for each task in the DB
fig = plot_aic_bic_fig(job_id)
aic_bic_plot = fig_to_png(fig)
response = make_response(aic_bic_plot.getvalue())
response.mimetype = 'image/png'
return response
def plot_correlation():
"""
Generate the Correlation heat map as a PNG
Parameters
----------
job_id: str
Returns
-------
image/png
"""
job_id = request.args.get('job_id')
if job_id is None:
return None
job = mongo_get_job(job_id)
s3_file_key = job['s3_file_key']
data = s3_to_df(s3_file_key)
fig = plot_correlation_fig(data)
correlation_plot = fig_to_png(fig)
response = make_response(correlation_plot.getvalue())
response.mimetype = 'image/png'
return response
