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main.py
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#!/usr/bin/env python3
import holidays
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pylab
from datafold.appfold import EDMD
from datafold.appfold.edmd import EDMDWindowPrediction
from datafold.dynfold import (
DiffusionMaps,
LocalRegressionSelection,
TSCTakensEmbedding,
)
from datafold.dynfold.dmd import DMDFull
from datafold.pcfold import (
GaussianKernel,
TSCDataFrame,
TSCMetric,
)
from datafold.utils.plot import (
plot_eigenvalues_time,
)
from matplotlib import rc
from pandas.api.types import is_timedelta64_dtype
from sklearn.model_selection import train_test_split
rc("text", usetex=True)
pylab.rcParams[
"text.latex.preamble"
] = r"\usepackage{tgheros} \usepackage{sansmath} \sansmath \usepackage{siunitx} \sisetup{detect-all}"
rc("font", size=12)
SINGLE_COLUMN = 8 # inches
ONEHALF_COLUMN = 10 # inches
DOUBLE_COLUMN = 14 # inches
class EDMDPositiveSensors(EDMD):
def _predict_ic(self, X_dict: TSCDataFrame, time_values, qois) -> TSCDataFrame:
_ret = super(EDMDPositiveSensors, self)._predict_ic(X_dict, time_values, qois)
_ret_sensors = _ret.loc[:, _ret.columns.str.startswith("sensor_")].copy()
_ret_sensors[_ret_sensors < 0] = 0
_ret.loc[:, _ret.columns.str.startswith("sensor_")] = _ret_sensors
return _ret
class EpsGaussianKernel(object):
def __init__(self, factor):
self.factor = float(factor)
def __call__(self, d):
return np.median(d) * self.factor
def __repr__(self):
return f"median(Distance)*{self.factor}"
def print_data_info(X):
print("======================================================")
print(f"X.shape={X.shape}")
print("----------------------------")
print(f"melbourne_data.n_timeseries={X.n_timeseries}")
print("----------------------------")
print(f"melbourne_data.n_samples={X.shape[0]}")
print("----------------------------")
print(f"melbourne_data.n_timesteps={X.n_timesteps}")
print("----------------------------")
print(f"melbourne_data.n_sensors={X.shape[1]}")
print("----------------------------")
print(f"check Null values: {X.isnull().sum(axis=0)}")
print("======================================================")
def filter_data(X, start_time, n_samples_ic, min_timesteps):
assert start_time < 24
if start_time is not None:
# start bocks such that the prediction starts at the same specified time
align_ts = list()
for _ts_id, _ts in X.itertimeseries():
if isinstance(_ts.index, pd.DatetimeIndex):
hour_per_day = _ts.index.hour
else:
hour_per_day = np.array(np.mod(_ts.index, 24))
ts_start_time = start_time - n_samples_ic + 1
if ts_start_time < 0:
ts_start_time = np.mod(24 - np.mod(ts_start_time, 24), 24)
try:
start_idx = np.argwhere(hour_per_day == ts_start_time)[0][0]
align_ts.append(_ts.iloc[start_idx:, :])
except Exception as e:
if _ts.shape[0] < 24 and ts_start_time not in hour_per_day:
# it can happen that there are only very short time series,
# these are dropped here already
pass
else:
raise e
X = TSCDataFrame.from_frame_list(align_ts)
n_timesteps = X.n_timesteps
if isinstance(X.n_timesteps, pd.Series):
drop_ids_timesteps = n_timesteps[X.n_timesteps < min_timesteps].index
if not drop_ids_timesteps.empty:
X = X.drop(drop_ids_timesteps, level=0)
X = X.tsc.assign_ids_sequential()
return X
def time_interval_and_sensor_selection(X):
# considered for publication, long time horizon, even number of sensors
X = X.loc[
:,
(
# "sensor_1_counts", #i:2009/03/24
"sensor_2_counts", # i:2009/03/30
# "sensor_3_counts", #i:2009/03/25
# "sensor_4_counts", #i:2009/03/23
# "sensor_5_counts", #i:2009/03/26
"sensor_6_counts", # i:2009/03/25
# "sensor_7_counts", #i:2014/12/17
# "sensor_8_counts", # i:2009/03/24
"sensor_9_counts", # i:2009/03/23
"sensor_10_counts", # i:2009/04/23
# "sensor_11_counts", #i:2009/01/20
# "sensor_12_counts", #i:2009/01/21
# "sensor_13_counts", #i:2009/03/24 # no
# "sensor_14_counts", #i:2019/09/25
# "sensor_15_counts", #i:2009/03/25
# "sensor_16_counts", #i:2009/03/30 || n:Device moved to location ID 53 (22/09/2015)
# "sensor_17_counts", #i:2009/03/30 || n:Device is upgraded in 26/02/2020
"sensor_18_counts", # i:2009/03/30
# "sensor_19_counts", # i:2013/09/02
# "sensor_20_counts", #i:2013/09/06
"sensor_21_counts", # i:2013/09/02
# "sensor_22_counts", #i:2013/08/12
# "sensor_23_counts", #i:2013/09/02
"sensor_24_counts", # i:2013/09/02
# "sensor_25_counts", # i:2019/10/02 || n:Sensor relocated from 14 to 25 on 2/10/2019
"sensor_26_counts", # i:2013/09/28
"sensor_27_counts", # i:2013/08/16
"sensor_28_counts", # i:2013/08/23
# "sensor_29_counts", #i:2013/10/11 || n:sensor upgraded from laser to 3D on 19/12/2019
# "sensor_30_counts", #i:2013/10/14
"sensor_31_counts", # i:2013/10/10
# "sensor_32_counts", #i:2013/12/20 || n:Device has been removed (24/01/2017)
# "sensor_33_counts", #i:2014/04/23
# "sensor_34_counts", #i:2014/06/08
# "sensor_35_counts", #i:2016/04/11
# "sensor_36_counts", #i:2015/01/20
# "sensor_37_counts", #i:2015/02/11
# "sensor_38_counts", #i:2014/12/05 || n:Device has been removed (17/02/2017)
# "sensor_39_counts", #i:2019/12/04 || n:In 4/12/2019 sensor upgraded
# "sensor_40_counts", #i:2015/01/19
# "sensor_41_counts", #i:2017/06/29
# "sensor_42_counts", #i:2015/04/15
# "sensor_43_counts", #i:2015/04/15
# "sensor_44_counts", #i:2015/04/15
# "sensor_45_counts", #i:2017/06/29
# "sensor_46_counts", #i:2017/07/10
# "sensor_47_counts", #i:2017/08/24
# "sensor_48_counts", #i:2017/10/02
# "sensor_49_counts", #i:2017/11/29
# "sensor_50_counts", #i:2017/11/30
# "sensor_51_counts", #i:2017/11/30
# "sensor_52_counts", #i:2017/07/31
# "sensor_53_counts", #i:2015/09/23
# "sensor_54_counts", #i:2018/06/26
# "sensor_55_counts", #i:2018/07/19
# "sensor_56_counts", #i:2018/07/25
# "sensor_57_counts", #i:2018/08/13
# "sensor_58_counts", #i:2018/09/27
# "sensor_59_counts", #i:2019/02/13
# "sensor_60_counts", #i:2019/03/08 || n:Temporary for the duration of the metro tunnel works. Installed under the scaffolding.
# "sensor_61_counts", #i:2019/06/28
# "sensor_62_counts", #i:2019/09/25
# "sensor_63_counts", #i:2020/01/07
# "sensor_64_counts", #i:2020/01/16
# "sensor_65_counts", #i:2020/03/12
# "sensor_66_counts", #i:2020/04/06
# "sensor_67_counts", #i:2020/06/03
# "sensor_68_counts", #i:2020/06/03
# "sensor_69_counts", #i:2020/06/03
# "sensor_70_counts", #i:2020/10/12
# "sensor_71_counts", #i:2020/10/16
# "sensor_72_counts", #i:2020/11/30
# "sensor_73_counts", #i:2020/10/02
# "sensor_75_counts", #i:2020/12/18
),
]
start_date = np.datetime64("2016-01-01T00:00:00")
end_date = np.datetime64("2020-01-01T00:00:00")
all_dates = X.index.get_level_values("time")
mask = (all_dates >= start_date) & (all_dates < end_date)
X = X.loc[mask]
return X
def read_and_select_data(filename):
melbourne_data: TSCDataFrame = TSCDataFrame.from_csv(filename, parse_dates=True)
melbourne_data = time_interval_and_sensor_selection(X=melbourne_data)
# Remove holidays on weekdays:
mask_holidays = np.array(
list(
map(
lambda x: x in holidays.Australia(prov="VIC"),
melbourne_data.time_values().astype(str),
)
)
)
weekdays = pd.DatetimeIndex(melbourne_data.time_values()).dayofweek < 5
mask_holidays = np.logical_and(mask_holidays, weekdays)
print(f"Remove {mask_holidays.sum()} hours from holidays.")
melbourne_data = melbourne_data.loc[~mask_holidays]
n_samples = melbourne_data.shape[0]
tsc_id_idx = pd.MultiIndex.from_arrays(
[np.zeros(n_samples), melbourne_data.index.get_level_values("time")]
)
melbourne_data = TSCDataFrame.from_same_indices_as(
indices_from=melbourne_data, values=melbourne_data, except_index=tsc_id_idx
)
for col in melbourne_data.columns:
sensor_column = melbourne_data.loc[:, col]
sensor_column = sensor_column.loc[sensor_column.isnull().sum(axis=1) == 0, :]
assigned = sensor_column.tsc.assign_ids_const_delta(drop_samples=True)
print(f"{col} has {assigned.n_timeseries} timeseries")
print(f"{col} has {assigned.delta_time} delta_time")
# melbourne_data = melbourne_data.groupby("ID").fillna(
# method="ffill", axis=0, limit=2
# )
melbourne_data = melbourne_data.tsc.assign_ids_const_delta(drop_samples=False)
melbourne_data = melbourne_data.loc[melbourne_data.isnull().sum(axis=1) == 0, :]
melbourne_data = melbourne_data.tsc.assign_ids_const_delta(drop_samples=True)
assert not melbourne_data.isnull().any().any()
delta_times = melbourne_data.delta_time
if isinstance(delta_times, pd.Series):
if is_timedelta64_dtype(delta_times):
_cmp = np.timedelta64(1, "h")
else:
_cmp = 1
drop_ids_delta_time = delta_times[delta_times != _cmp].index
melbourne_data = melbourne_data.drop(drop_ids_delta_time, level=0)
melbourne_data = melbourne_data.tsc.assign_ids_sequential()
print_data_info(melbourne_data)
return melbourne_data
def setup_basic_edmd():
takens_delays = 168
takens = (
"takens",
TSCTakensEmbedding(
delays=takens_delays,
kappa=0, # kappa values larger than 0 did not show improvement
),
)
laplace = (
"laplace",
DiffusionMaps(
kernel=GaussianKernel(epsilon=EpsGaussianKernel(factor=1)),
n_eigenpairs=500, # 500 optimal in one CV
alpha=1,
time_exponent=0, # -0.5 is a scaling factor by Giannakis for the DMAP
symmetrize_kernel=True,
dist_kwargs=dict(cut_off=np.inf), # exact_numeric=False, n_jobs=-1
),
)
edmd = EDMDPositiveSensors(
dict_steps=[takens, laplace],
dmd_model=DMDFull(is_diagonalize=True),
include_id_state=False,
sort_koopman_triplets=True,
verbose=True,
)
prediction_window = int(24)
n_samples_ic = takens_delays + 1
window_size = prediction_window + n_samples_ic
edmd = EDMDWindowPrediction(
window_size=window_size, offset=prediction_window
).adapt_model(edmd)
print("Model: \n", edmd)
return edmd, n_samples_ic
def plot_sensor_time_series(
X_original,
X_reconstruct_train,
diff_train,
initial_states_train,
X_reconstruct_test,
diff_test,
initial_states_test,
):
sensor_columns = X_original.columns[X_original.columns.str.startswith("sensor_")]
sensor_columns = ["sensor_2_counts", "sensor_27_counts"]
for _col_sensor in sensor_columns:
f, ax = plt.subplots(
2,
1,
sharex=True,
gridspec_kw=dict(height_ratios=[5, 1]),
figsize=[15, 9],
)
_df = X_original.loc[:, _col_sensor]
_df_pred_train = X_reconstruct_train.loc[:, _col_sensor]
_df_diff_train = diff_train.loc[:, _col_sensor]
_df_initial_states_train = initial_states_train.loc[:, [_col_sensor]]
_df_pred_test = X_reconstruct_test.loc[:, _col_sensor]
_df_diff_test = diff_test.loc[:, _col_sensor]
_df_initial_states_test = initial_states_test.loc[:, [_col_sensor]]
ax[0].set_title(_col_sensor)
ax[0].grid()
for _ts_id, _ts in TSCDataFrame(_df).itertimeseries():
ax[0].plot(_ts.index, _ts.to_numpy(), c="black")
_df_pred_train.plot(ax=ax[0], legend=False, color="green")
ax[0].plot(
_df_initial_states_train.time_values(),
_df_initial_states_train.to_numpy().ravel(),
"o",
markersize=5,
c="green",
)
_df_pred_test.plot(ax=ax[0], legend=False, color="red")
ax[0].plot(
_df_initial_states_test.time_values(),
_df_initial_states_test.to_numpy().ravel(),
"o",
markersize=5,
c="red",
)
index = np.hstack(
[
_df_pred_train.time_values_delta_time(),
_df_pred_test.time_values_delta_time(),
]
)
_tmp_train = pd.DataFrame(_df_pred_train).droplevel(0)
_tmp_test = pd.DataFrame(_df_pred_test).droplevel(0)
upper_limit = (
max(
[
float(_df_pred_train.max()),
float(_df_pred_test.max()),
float(_df.max(axis=0)),
]
)
* 1.1
)
ax[0].set_ylim([-50, upper_limit])
def plot_paper_data(train_sensor_data, test_sensor_data, n_samples_ic):
sensors = train_sensor_data.columns
f, ax = plt.subplots(
nrows=len(sensors),
sharex=True,
sharey=True,
figsize=(SINGLE_COLUMN, SINGLE_COLUMN * 0.85),
)
f.subplots_adjust(hspace=0, left=0.207, right=0.917, top=0.94, bottom=0.19)
for i, col in enumerate(sensors):
_train = train_sensor_data.loc[:, [col]]
for j, ts in _train.itertimeseries():
ts_warm_up = ts.iloc[:n_samples_ic, :]
ts_data = ts.iloc[n_samples_ic:, :]
ax[i].fill_between(
ts_warm_up.index, y1=ts_warm_up.to_numpy().ravel(), y2=0, color="black"
)
ax[i].fill_between(
ts_data.index, y1=ts_data.to_numpy().ravel(), y2=0, color="skyblue"
)
_test = test_sensor_data.loc[:, col]
for j, ts in _test.itertimeseries():
ts_warm_up = ts.iloc[:n_samples_ic, :]
ts_data = ts.iloc[n_samples_ic:, :]
ax[i].fill_between(
ts_warm_up.index, y1=ts_warm_up.to_numpy().ravel(), y2=0, color="black"
)
ax[i].fill_between(
ts_data.index, y1=ts_data.to_numpy().ravel(), y2=0, color="lightcoral"
)
ax[i].axes.set_yticklabels([])
ax[i].axes.set_yticks([0, 7000])
ax[i].set_ylim([0, 7000])
ax[i].set_ylabel(col.replace("sensor_", "").replace("_counts", ""), rotation=0)
ax[i].set_xlim([np.datetime64("2016-01-01"), np.datetime64("2019-12-31")])
ax[i].axes.set_xticks(
[
np.datetime64("2016-01-01"), # start training
np.datetime64("2017-01-01"), # start training
# np.datetime64("2018-08-06"), # end training
np.datetime64("2018-09-01"), # start test
np.datetime64("2019-12-31"), # end test
]
)
ax[i].axes.set_xticklabels(["2016.1", "2017.1", "2018.9", "2020.1"])
f.add_subplot(111, frame_on=False)
plt.tick_params(labelcolor="none", bottom=False, left=False)
plt.xlabel("time [year.month]")
plt.ylabel("sensor ID (s)")
def plot_error_table(
edmd,
X_windows_train: TSCDataFrame,
X_reconstruct_train: TSCDataFrame,
X_windows_test: TSCDataFrame,
X_reconstruct_test: TSCDataFrame,
):
def predict_naive(X_windows, X_reconstruct):
X_reconstruct_naive = []
for i, df in X_reconstruct.itertimeseries():
last_week_index = df.index - np.timedelta64(7, "D")
naive_df = X_windows.loc[pd.IndexSlice[i, last_week_index], :]
naive_df.index = pd.MultiIndex.from_product([[i], df.index.copy()])
X_reconstruct_naive.append(naive_df)
X_reconstruct_naive = TSCDataFrame.from_frame_list(X_reconstruct_naive)
return X_reconstruct_naive
X_reconstruct_train_naive = predict_naive(
X_windows=X_windows_train, X_reconstruct=X_reconstruct_train
)
X_reconstruct_test_naive = predict_naive(
X_windows=X_windows_test, X_reconstruct=X_reconstruct_test
)
# COMPUTE MER
mer_true = pd.DataFrame(X_windows_test.loc[X_reconstruct_test.index, :]).copy()
mer_pred = pd.DataFrame(X_reconstruct_test).copy()
mer_true = mer_true.drop(mer_true.groupby("ID").head(1).index, axis=0)
mer_pred = mer_pred.drop(mer_pred.groupby("ID").head(1).index, axis=0)
nominator = (mer_true - mer_pred).abs()
denom = mer_true.groupby("ID").mean()
nominator = pd.DataFrame(nominator.drop(55, axis=0, level=0))
denom = pd.DataFrame(denom.drop(55, axis=0))
mer = (nominator / denom).groupby("ID").sum()
mer = (100 * 1 / 24 * mer).mean(axis=0)
mer = np.mean(mer)
print(f"MER on entire test set {mer}")
edmd.metric_eval = TSCMetric(metric="mae", mode="feature", scaling="min-max")
edmd_score_train = edmd._score_eval(
X_windows_train.loc[X_reconstruct_train.index, :], X_reconstruct_train
)
naive_score_train = edmd._score_eval(
X_windows_train.loc[X_reconstruct_train.index, :], X_reconstruct_train_naive
)
edmd_score_test = edmd._score_eval(
X_windows_test.loc[X_reconstruct_test.index, :], X_reconstruct_test
)
naive_score_test = edmd._score_eval(
X_windows_test.loc[X_reconstruct_test.index, :], X_reconstruct_test_naive
)
print(f"EDMD score train = {edmd_score_train}")
print(f"Naive score train = {naive_score_train}")
print(f"EDMD score test = {edmd_score_test}")
print(f"Naive score test = {naive_score_test}")
y_true_train = X_windows_train.loc[X_reconstruct_train.index]
y_pred_train = X_reconstruct_train
y_true_test = X_windows_test.loc[X_reconstruct_test.index]
y_pred_test = X_reconstruct_test
idx = [
i.replace("_counts", "").replace("sensor_", "") for i in X_windows_test.columns
]
scale = scale_sensors(X_windows=X_windows_train.loc[X_reconstruct_train.index, :])
error_table = pd.DataFrame(
scale.astype(int).astype(str).to_numpy(),
index=idx,
columns=pd.MultiIndex.from_arrays([[""], ["$Q_{95\%}$"]]),
)
col = pd.MultiIndex.from_product(
[["training", "test"], ["mean $\pm$ std", "mean", "std", "RRMSE", "RRMSE(b)"]]
)
error_table = pd.concat(
[error_table, pd.DataFrame("", index=idx, columns=col)], axis=1
)
error_table.loc[:, ("training", "mean")] = (
(y_true_train - y_pred_train).mean().to_numpy()
)
error_table.loc[:, ("training", "std")] = (
(y_true_train - y_pred_train).std().to_numpy()
)
error_table.loc[:, ("test", "mean")] = (y_true_test - y_pred_test).mean().to_numpy()
error_table.loc[:, ("test", "std")] = (y_true_test - y_pred_test).std().to_numpy()
error_metric = TSCMetric(metric="rmse", mode="feature")
train_error_edmd = (
error_metric(
y_true=y_true_train,
y_pred=y_pred_train,
)
/ scale
* 100
).to_numpy()
train_error_naive = (
error_metric(
X_windows_train.loc[X_reconstruct_train_naive.index, :],
X_reconstruct_train_naive,
)
/ scale
* 100
).to_numpy()
test_error_edmd = (
error_metric(y_true=y_true_test, y_pred=y_pred_test).to_numpy() / scale * 100
).to_numpy()
test_error_naive = (
error_metric(
X_windows_test.loc[X_reconstruct_test.index, :], X_reconstruct_test_naive
)
/ scale
* 100
).to_numpy()
from numpy.core.defchararray import add as npaddstr
error_table.loc[:, ("training", "RRMSE")] = np.round(train_error_edmd, 2).astype(
str
)
error_table.loc[:, ("test", "RRMSE")] = np.round(test_error_edmd, 2).astype(str)
error_table.loc[:, ("training", "RRMSE(b)")] = np.round(
train_error_naive, 2
).astype(str)
error_table.loc[:, ("test", "RRMSE(b)")] = np.round(test_error_naive, 2).astype(str)
avr = lambda vals: np.round(np.average(vals), 2).astype(str)
weight_avr = lambda vals: np.average(vals, weights=scale)
error_table.loc["agg.", :] = [
"", # scale
"", # mean pm std
weight_avr(error_table.loc[:, ("training", "mean")]),
np.sqrt(
weight_avr(np.square(error_table.loc[:, ("training", "std")].to_numpy()))
),
avr(error_table.loc[:, ("training", "RRMSE")].astype(float)),
avr(error_table.loc[:, ("training", "RRMSE(b)")].astype(float)),
"", # mean pm std
np.round(weight_avr(error_table.loc[:, ("test", "mean")]), 2),
np.sqrt(weight_avr(np.square(error_table.loc[:, ("test", "std")].to_numpy()))),
avr(error_table.loc[:, ("test", "RRMSE")].astype(float)),
avr(error_table.loc[:, ("test", "RRMSE(b)")].astype(float)),
]
def mean_pm_std(_mean, _std):
return [
f"{np.round(i).astype(int)} $\pm$ {np.round(j).astype(int)}"
for i, j in zip(
_mean,
_std,
)
]
error_table.loc[:, ("training", "mean $\pm$ std")] = mean_pm_std(
error_table.loc[:, ("training", "mean")].to_numpy(),
error_table.loc[:, ("training", "std")].to_numpy(),
)
error_table.loc[:, ("test", "mean $\pm$ std")] = mean_pm_std(
error_table.loc[:, ("test", "mean")].to_numpy(),
error_table.loc[:, ("test", "std")].to_numpy(),
)
error_table = error_table.drop(error_table.columns[[2, 3, 7, 8]], axis=1)
error_table.loc[:, error_table.columns[[2, 3, 5, 6]]] = npaddstr(
error_table.loc[:, error_table.columns[[2, 3, 5, 6]]].to_numpy().astype(str),
" \%",
)
error_table.columns = error_table.columns.set_levels(
[
"",
f"test ($C_{{\\text{{test}}}} = {X_reconstruct_test.n_timeseries}$)",
f"training ($C_{{\\text{{train}}}} = {X_reconstruct_train.n_timeseries}$)",
],
level=0,
)
error_table.index.name = "ID ($s$)"
error_table = error_table.reset_index(col_level=1)
return error_table
def plot_paper_week_timeseries(X_windows_test, X_reconstruct_test):
ic_time = pd.DatetimeIndex(
X_windows_test.initial_states().index.get_level_values("time")
)
mondays_idx = np.where(ic_time.dayofweek == 0)[0] # 0 = Monday, 6 = Sunday
mondays_idx = mondays_idx[mondays_idx + 14 < len(ic_time)]
timedelta = ic_time[mondays_idx + 14] - ic_time[mondays_idx]
X_windows_test = X_windows_test.loc[X_reconstruct_test.index, :]
sensor_selection = [f"sensor_{s}_counts" for s in [2, 9, 28, 31]]
fortnights_ids = [113] # 215
for plot_id in fortnights_ids:
plot_data_test = X_windows_test.loc[plot_id : plot_id + 6, :]
plot_data_reconstruct = X_reconstruct_test.loc[plot_id : plot_id + 6, :]
f, ax = plt.subplots(
nrows=len(sensor_selection),
sharex=True,
figsize=(DOUBLE_COLUMN, DOUBLE_COLUMN * 0.55),
)
f.subplots_adjust(bottom=0.136, top=0.893)
for i, sensor in enumerate(sensor_selection):
sensor_data_test = plot_data_test.loc[:, sensor]
sensor_data_reconstruct = plot_data_reconstruct.loc[:, sensor]
is_last = i == len(sensor_selection) - 1
ics = sensor_data_reconstruct.initial_states()
ax[i].plot(
ics.index.get_level_values("time"),
ics.to_numpy().ravel(),
"o",
markersize=5,
color="mediumblue",
label="initial condition" if is_last else None,
)
ax[i].plot(
sensor_data_test.index.get_level_values("time"),
sensor_data_test.to_numpy().ravel(),
color="black",
label="true values" if is_last else None,
)
ax[i].plot(
sensor_data_reconstruct.index.get_level_values("time"),
sensor_data_reconstruct.to_numpy(),
color="red",
alpha=0.7,
label="predicted values" if is_last else None,
)
ax[i].set_xticks(
np.arange(
np.datetime64("2019-02-11"),
np.datetime64("2019-02-19"),
np.timedelta64(1, "D"),
)
)
ax[i].set_xticklabels([f"{i}.02." for i in range(11, 19)])
ax[i].set_xlim([np.datetime64("2019-02-11"), np.datetime64("2019-02-18")])
max_val = np.max(sensor_data_test.to_numpy().ravel())
ytick = np.round(max_val, decimals=-2)
ytick = int(ytick)
ax[i].set_yticks([max_val // 2, ytick])
ax[i].set_yticklabels(["", ""])
ax[i].set_ylim([0, ytick])
ax_right = ax[i].twinx()
ax_right.set_ylabel(
sensor.replace("sensor_", "").replace("_counts", ""),
labelpad=10,
rotation=0,
)
ax_right.set_yticks([])
ax[i].set_ylabel(ytick, labelpad=10, rotation=0)
ax[i].grid()
f.align_ylabels()
if is_last:
f.legend(loc="upper center", ncol=3)
ax = f.add_subplot(111, frame_on=False)
ax1 = ax.twinx()
ax.tick_params(labelcolor="none", bottom=False, left=False, right=False)
ax.set_xlabel("time (year 2019)")
ax.set_ylabel("pedestrian count", labelpad=10)
ax1.tick_params(labelcolor="none", bottom=False, left=False, right=False)
ax1.set_ylabel("sensor ID", labelpad=5, rotation=-90)
def scale_sensors(X_windows):
quantile95 = pd.DataFrame(X_windows).quantile(q=0.95)
return quantile95
def plot_paper_sensor_profile(
scale,
X_windows_test,
X_reconstruct_test,
):
sensor_columns = X_reconstruct_test.columns[
X_reconstruct_test.columns.str.startswith("sensor_")
]
X_reconstruct_test = X_reconstruct_test.loc[:, sensor_columns]
X_reconstruct_test = X_reconstruct_test.drop(
X_reconstruct_test.groupby("ID").head(1).index, axis=0
)
X_windows_test = X_windows_test.drop(
X_windows_test.groupby("ID").head(1).index, axis=0
)
X_win_scaled = X_windows_test.loc[X_reconstruct_test.index, :] / scale
X_reconstruct_scaled = X_reconstruct_test / scale
sensor_columns = X_windows_test.columns[
X_windows_test.columns.str.startswith("sensor_")
]
f, ax = plt.subplots(
nrows=3,
ncols=len(sensor_columns) + 3,
figsize=(DOUBLE_COLUMN, DOUBLE_COLUMN * 0.5),
gridspec_kw={
"width_ratios": np.append(np.ones(len(sensor_columns) + 1), [0.3, 0.2])
},
)
gs = ax[0, -1].get_gridspec()
# from https://matplotlib.org/3.1.1/gallery/subplots_axes_and_figures/gridspec_and_subplots.html
# remove the underlying axes
for _ax in ax[0:2, -1].ravel():
_ax.remove()
axcbar_vals = f.add_subplot(gs[0:2, -1])
# f.suptitle(column)
f.subplots_adjust(
hspace=0.14, wspace=0.02, bottom=0.138, top=0.921, left=0.088, right=0.943
)
# left=0.048, bottom=0.1, right=0.9, # top=0.974,
ax[0][-2].remove()
ax[1][-2].remove()
ax[2][-2].remove()
# ax[1][-1].remove()
cmap = "GnBu"
for i, column in enumerate(sensor_columns):
is_first = i == 0
is_last = i == len(sensor_columns) - 1
true_values = X_win_scaled.loc[:, column]
pred_values = X_reconstruct_scaled.loc[:, column]
true_values = true_values.to_numpy().reshape(
[true_values.n_timeseries, true_values.n_timesteps]
)
pred_values = pred_values.to_numpy().reshape(
[pred_values.n_timeseries, pred_values.n_timesteps]
)
vmin, vmax = 0, 1
vals = ax[0][i].imshow(
true_values, vmin=vmin, vmax=vmax, aspect="auto", cmap=cmap
)
ax[0][i].set_title(
column.replace("sensor_", "").replace("_counts", ""),
# + f"\n({int(mean_max_val_per_day[i]):d})"
)
ax[1][i].imshow(pred_values, vmin=vmin, vmax=vmax, aspect="auto", cmap=cmap)
vmin, vmax = -0.5, 0.5
ax[2][i].imshow(
pred_values - true_values, aspect="auto", vmin=vmin, vmax=vmax, cmap="bwr"
)
# ax[2][i].set_xlabel(
# f"{int(mean_absolute_error(X_windows_test.loc[:, column], X_reconstruct_test.loc[:, column]))}"
# )
if is_first:
ax[0][0].set_xticks([])
ax[1][0].set_xticks([])
ax[2][0].set_xticks([0, 23])
ax[2][0].set_xticklabels([1, 24])
ax[0][0].set_yticks([0, 100, 200, 300])
ax[1][0].set_yticks([0, 100, 200, 300])
ax[2][0].set_yticks([0, 100, 200, 300])
else:
ax[0][i].set_xticks([])
ax[1][i].set_xticks([])
ax[2][i].set_xticks([23])
ax[2][i].set_xticklabels([24])
ax[0][i].set_yticks([])
ax[1][i].set_yticks([])
ax[2][i].set_yticks([])
if is_last:
cbar = plt.colorbar(vals, axcbar_vals, ticks=[0, 0.5, 1], extend="max")
cbar.ax.set_yticklabels(["0", "0.5", "1"])
mean_true_values = TSCDataFrame(
np.average(X_win_scaled.values, axis=1),
index=X_win_scaled.index,
columns=["average"],
)
mean_true_values = mean_true_values.to_numpy().reshape(
[mean_true_values.n_timeseries, mean_true_values.n_timesteps]
)
mean_pred_values = TSCDataFrame(
np.average(
X_reconstruct_scaled.values, axis=1
), # , weights=mean_max_val_per_day
index=X_win_scaled.index,
columns=["average"],
)
mean_pred_values = mean_pred_values.to_numpy().reshape(
[mean_pred_values.n_timeseries, mean_pred_values.n_timesteps]
)
c = -3
ax[0][c].imshow(mean_true_values, vmin=0, vmax=1, aspect="auto", cmap=cmap)
ax[1][c].imshow(mean_pred_values, vmin=0, vmax=1, aspect="auto", cmap=cmap)
err = ax[2][c].imshow(
mean_pred_values - mean_true_values,
vmin=-0.5,
vmax=0.5,
cmap="bwr",
aspect="auto",
)
cbar = plt.colorbar(err, ax[2][-1], ticks=[-0.5, 0, 0.5], extend="both")
cbar.ax.set_yticklabels([-0.5, 0, 0.5])
ax[0][c].set_title("agg.")
ax[0][c].set_xticks([])
ax[1][c].set_xticks([])
ax[2][c].set_xticks([23])
ax[2][c].set_xticklabels([24])
ax[0][c].set_yticks([])
ax[1][c].set_yticks([])
ax[2][c].set_yticks([])
ax[0][c].set_ylabel("true values", labelpad=10, rotation=-90)
ax[1][c].set_ylabel("pred. values", labelpad=10, rotation=-90)
ax[2][c].set_ylabel("difference", labelpad=10, rotation=-90)
ax[0][c].yaxis.set_label_position("right")
ax[1][c].yaxis.set_label_position("right")
ax[2][c].yaxis.set_label_position("right")
f.add_subplot(111, frame_on=False)
plt.tick_params(labelcolor="none", bottom=False, left=False)
plt.xlabel("time [hour]")
plt.ylabel("initial condition")
def plot_timeseries_dmap(
sensor_str, dmap_idx, X_windows_train, X_windows_test, X_latent_train, X_latent_test
):
f, ax = plt.subplots(
nrows=2, sharex=True, figsize=(DOUBLE_COLUMN, DOUBLE_COLUMN * 0.55)
)
X_windows_train.loc[:, sensor_str].plot(
ax=ax[0], legend=False, ylabel="", c="black"
)
X_windows_test.loc[:, sensor_str].plot(
ax=ax[0], ylabel="", legend=False, c="black"
)
# -1 bc. in paper is 1-indexed, in code 0-indexed
X_latent_train.loc[:, f"dmap{dmap_idx-1}"].plot(ax=ax[1], legend=False, c="black")
X_latent_test.loc[:, f"dmap{dmap_idx-1}"].plot(ax=ax[1], legend=False, c="black")
ax[0].set_ylabel(sensor_str.replace("_", ""))
ax[1].set_ylabel(fr"$\varphi_{{{dmap_idx}}}$")
def plot_paper_dmap_selection(
edmd,
selection,
X_latent_test,
):
dmap_eigenvalues = edmd.named_steps["laplace"].eigenvalues_
print(f"Smallest DMAP eigenvalue = {dmap_eigenvalues[-1]}")
sensor_mask = X_latent_test.columns.str.startswith("sensor_")
X_latent_test = X_latent_test.loc[:, ~sensor_mask]
plot_data = X_latent_test.iloc[:, selection]
ncols = plot_data.shape[1] // 2
f, ax = plt.subplots(
nrows=2, ncols=ncols, figsize=(DOUBLE_COLUMN * 0.8, ONEHALF_COLUMN * 0.8)
)
f.subplots_adjust(left=0.148, bottom=0.155, right=0.978, top=0.921, hspace=0.248)
vmin = None
vmax = None
for i, col in enumerate(plot_data.columns):
values = plot_data.loc[:, [col]].to_numpy()
values = values.reshape(plot_data.n_timeseries, plot_data.n_timesteps)
values = values[:100, :]
_ax = ax[i // ncols][np.mod(i, ncols)]
_ax.imshow(values, vmin=vmin, vmax=vmax, aspect="auto")
_ax.set_title(i + 1)
is_first_in_row = np.mod(i, ncols) == 0
is_first_row = i // ncols == 0
dmap_idx = int(col.replace("dmap", ""))
_ax.set_title(
rf"$\varphi_{{{dmap_idx+1}}}$",
verticalalignment="center",