Switchback calendar visualization

This notebook shows the visualizations of different calendars that can be generated with switchback splitters

In [1]:

from cluster_experiments import PowerAnalysis
import pandas as pd
import numpy as np
from plotnine import ggplot, geom_tile, element_text, element_blank, theme, labs, facet_wrap, aes, theme_set, theme_minimal
# Remove warnings
import warnings
warnings.filterwarnings('ignore')

np.random.seed(42)

theme_set(theme_minimal())

from cluster_experiments import PowerAnalysis import pandas as pd import numpy as np from plotnine import ggplot, geom_tile, element_text, element_blank, theme, labs, facet_wrap, aes, theme_set, theme_minimal # Remove warnings import warnings warnings.filterwarnings('ignore') np.random.seed(42) theme_set(theme_minimal())

In [2]:

def plot_calendar(splitter, df, calendar_name):
    treatments = splitter.assign_treatment_df(df)
    
    treatment_df = (
        treatments
        .loc[:, ["city", "treatment", "time"]]
        .drop_duplicates()
    )
    # Convert time column to datetime format and extract week and day
    treatment_df['time'] = pd.to_datetime(treatment_df['time'])
    treatment_df['week'] = treatment_df['time'].dt.isocalendar().week.astype(str)
    treatment_df['day'] = treatment_df['time'].dt.dayofweek.astype(str)
    # day of week with format number + name
    treatment_df['day_of_week'] = treatment_df['day'] + ' - ' + treatment_df['time'].dt.day_name()

    return (ggplot(treatment_df, aes(x='day_of_week', y='week', fill='treatment'))
            + geom_tile()
            + facet_wrap('city', nrow=2)
            + labs(title=f'Switchback calendar in different cities, using {calendar_name}', x='Day', y='Week')
            + theme(axis_text_x=element_text(angle=90, vjust=1.0),
                    axis_text_y=element_text(size=8),
                    strip_background=element_blank(),
                    strip_text=element_text(size=10)))

def plot_calendar(splitter, df, calendar_name): treatments = splitter.assign_treatment_df(df) treatment_df = ( treatments .loc[:, ["city", "treatment", "time"]] .drop_duplicates() ) # Convert time column to datetime format and extract week and day treatment_df['time'] = pd.to_datetime(treatment_df['time']) treatment_df['week'] = treatment_df['time'].dt.isocalendar().week.astype(str) treatment_df['day'] = treatment_df['time'].dt.dayofweek.astype(str) # day of week with format number + name treatment_df['day_of_week'] = treatment_df['day'] + ' - ' + treatment_df['time'].dt.day_name() return (ggplot(treatment_df, aes(x='day_of_week', y='week', fill='treatment')) + geom_tile() + facet_wrap('city', nrow=2) + labs(title=f'Switchback calendar in different cities, using {calendar_name}', x='Day', y='Week') + theme(axis_text_x=element_text(angle=90, vjust=1.0), axis_text_y=element_text(size=8), strip_background=element_blank(), strip_text=element_text(size=10)))

In [3]:

# Define data with random dates
df_raw = pd.DataFrame(
    {   
        "time": pd.date_range("2021-03-01", "2021-03-15", freq="1min")[
            np.random.randint(14 * 24 * 60, size=14 * 24 * 60)
        ],
        "y": np.random.randn(14 * 24 * 60),
    }
).assign(
    day_of_week=lambda df: df.time.dt.dayofweek,
    hour_of_day=lambda df: df.time.dt.hour,
    week=lambda df: df.time.dt.isocalendar().week,
)
df = pd.concat([df_raw.assign(city=city) for city in ("TGN", "NYC", "LON", "REU")])

# Define data with random dates df_raw = pd.DataFrame( { "time": pd.date_range("2021-03-01", "2021-03-15", freq="1min")[ np.random.randint(14 * 24 * 60, size=14 * 24 * 60) ], "y": np.random.randn(14 * 24 * 60), } ).assign( day_of_week=lambda df: df.time.dt.dayofweek, hour_of_day=lambda df: df.time.dt.hour, week=lambda df: df.time.dt.isocalendar().week, ) df = pd.concat([df_raw.assign(city=city) for city in ("TGN", "NYC", "LON", "REU")])

In [4]:

# We have data of 2-weeks in 4 cities
df.head()

# We have data of 2-weeks in 4 cities df.head()

Out[4]:

	time	y	day_of_week	hour_of_day	week	city
0	2021-03-11 23:15:00	0.827658	3	23	10	TGN
1	2021-03-01 14:20:00	0.843550	0	14	9	TGN
2	2021-03-04 17:50:00	0.306790	3	17	9	TGN
3	2021-03-09 07:24:00	-2.133119	1	7	10	TGN
4	2021-03-08 20:04:00	-1.026710	0	20	10	TGN

In [5]:

# Completely random calendar
config = {
    "time_col": "time",
    "switch_frequency": "1d",
    "perturbator": "constant",
    "analysis": "ols_clustered",
    "splitter": "switchback",
    "cluster_cols": ["time", "city"],
    "target_col": "y",
}

power = PowerAnalysis.from_dict(config)

plot_calendar(power.splitter, df, "random calendar")

# Completely random calendar config = { "time_col": "time", "switch_frequency": "1d", "perturbator": "constant", "analysis": "ols_clustered", "splitter": "switchback", "cluster_cols": ["time", "city"], "target_col": "y", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, "random calendar")

Out[5]:

<ggplot: (310936046)>

In [6]:

# Balanced calendar
config = {
    "time_col": "time",
    "switch_frequency": "1d",
    "perturbator": "constant",
    "analysis": "ols_clustered",
    "splitter": "switchback_balance",
    "cluster_cols": ["time", "city"],
    "target_col": "y",
}

power = PowerAnalysis.from_dict(config)

plot_calendar(power.splitter, df, "balanced Calendar")

# Balanced calendar config = { "time_col": "time", "switch_frequency": "1d", "perturbator": "constant", "analysis": "ols_clustered", "splitter": "switchback_balance", "cluster_cols": ["time", "city"], "target_col": "y", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, "balanced Calendar")

Out[6]:

<ggplot: (311115590)>

In [7]:

# Stratified - city level
config = {
    "time_col": "time",
    "switch_frequency": "1d",
    "perturbator": "constant",
    "analysis": "ols_clustered",
    "splitter": "switchback_stratified",
    "cluster_cols": ["time", "city"],
    "strata_cols": ["city"],
    "target_col": "y",
}

power = PowerAnalysis.from_dict(config)

plot_calendar(power.splitter, df, "stratified calendar (city level)")

# Stratified - city level config = { "time_col": "time", "switch_frequency": "1d", "perturbator": "constant", "analysis": "ols_clustered", "splitter": "switchback_stratified", "cluster_cols": ["time", "city"], "strata_cols": ["city"], "target_col": "y", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, "stratified calendar (city level)")

Out[7]:

<ggplot: (311223354)>

In [8]:

# Stratified - city + week level
config = {
    "time_col": "time",
    "switch_frequency": "1d",
    "perturbator": "constant",
    "analysis": "ols_clustered",
    "splitter": "switchback_stratified",
    "cluster_cols": ["time", "city"],
    "strata_cols": ["week", "city"],
    "target_col": "y",
}

power = PowerAnalysis.from_dict(config)

plot_calendar(power.splitter, df, "stratified calendar (week + city level)")

# Stratified - city + week level config = { "time_col": "time", "switch_frequency": "1d", "perturbator": "constant", "analysis": "ols_clustered", "splitter": "switchback_stratified", "cluster_cols": ["time", "city"], "strata_cols": ["week", "city"], "target_col": "y", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, "stratified calendar (week + city level)")

Out[8]:

<ggplot: (311217083)>

In [9]:

# Stratified - city + day of week level
config = {
    "time_col": "time",
    "switch_frequency": "1d",
    "perturbator": "constant",
    "analysis": "ols_clustered",
    "splitter": "switchback_stratified",
    "cluster_cols": ["time", "city"],
    "strata_cols": ["day_of_week", "city"],
    "target_col": "y",
}

power = PowerAnalysis.from_dict(config)

plot_calendar(power.splitter, df, "stratified calendar (day of week + city level)")

# Stratified - city + day of week level config = { "time_col": "time", "switch_frequency": "1d", "perturbator": "constant", "analysis": "ols_clustered", "splitter": "switchback_stratified", "cluster_cols": ["time", "city"], "strata_cols": ["day_of_week", "city"], "target_col": "y", } power = PowerAnalysis.from_dict(config) plot_calendar(power.splitter, df, "stratified calendar (day of week + city level)")

Out[9]:

<ggplot: (311416839)>

In [ ]: