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fix: convert level to quantiles in historic forecast #510

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merged 3 commits into from
Oct 29, 2024
Merged

fix: convert level to quantiles in historic forecast #510

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0837919
fix: convert level to quantiles in historic forecast
jmoralez Oct 29, 2024
dd7470e
fix name
jmoralez Oct 29, 2024
e3c42dd
set tols
jmoralez Oct 29, 2024
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18 changes: 12 additions & 6 deletions nbs/src/nixtla_client.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -1161,7 +1161,6 @@
" )\n",
" out = ufp.assign_columns(out, 'TimeGPT', resp['mean'])\n",
" out = _maybe_add_intervals(out, resp['intervals'])\n",
" out = _maybe_convert_level_to_quantiles(out, quantiles)\n",
" if add_history:\n",
" in_sample_df = _parse_in_sample_output(\n",
" in_sample_output=in_sample_resp,\n",
Expand All @@ -1173,6 +1172,7 @@
" )\n",
" in_sample_df = ufp.drop_columns(in_sample_df, target_col)\n",
" out = ufp.vertical_concat([in_sample_df, out])\n",
" out = _maybe_convert_level_to_quantiles(out, quantiles)\n",
" self._maybe_assign_feature_contributions(\n",
" expected_contributions=feature_contributions,\n",
" resp=resp,\n",
Expand All @@ -1184,8 +1184,10 @@
" sort_idxs = ufp.maybe_compute_sort_indices(out, id_col=id_col, time_col=time_col)\n",
" if sort_idxs is not None:\n",
" out = ufp.take_rows(out, sort_idxs)\n",
" out = ufp.drop_index_if_pandas(out)\n",
" if hasattr(self, 'feature_contributions'):\n",
" self.feature_contributions = ufp.take_rows(self.feature_contributions, sort_idxs)\n",
" self.feature_contributions = ufp.drop_index_if_pandas(self.feature_contributions)\n",
" out = _maybe_drop_id(df=out, id_col=id_col, drop=drop_id)\n",
" self._maybe_assign_weights(weights=resp['weights_x'], df=df, x_cols=x_cols)\n",
" return out\n",
Expand Down Expand Up @@ -1833,8 +1835,10 @@
" **kwargs\n",
" )\n",
" assert all(col in df_qls.columns for col in exp_q_cols)\n",
" assert not any('-lo-' in col for col in df_qls.columns)\n",
" # test monotonicity of quantiles\n",
" df_qls.apply(lambda x: x.is_monotonic_increasing, axis=1).sum() == len(exp_q_cols)\n",
" for c1, c2 in zip(exp_q_cols[:-1], exp_q_cols[1:]):\n",
" assert df_qls[c1].lt(df_qls[c2]).all()\n",
"test_method_qls(nixtla_client.forecast)\n",
"test_method_qls(nixtla_client.forecast, add_history=True)\n",
"test_method_qls(nixtla_client.cross_validation)"
Expand Down Expand Up @@ -2394,8 +2398,8 @@
"anom_inferred_df_index = nixtla_client.detect_anomalies(df_ds_index)\n",
"fcst_inferred_df = nixtla_client.forecast(df_[['ds', 'unique_id', 'y']], h=10)\n",
"anom_inferred_df = nixtla_client.detect_anomalies(df_[['ds', 'unique_id', 'y']])\n",
"pd.testing.assert_frame_equal(fcst_inferred_df_index, fcst_inferred_df, atol=1e-3)\n",
"pd.testing.assert_frame_equal(anom_inferred_df_index, anom_inferred_df, atol=1e-3)\n",
"pd.testing.assert_frame_equal(fcst_inferred_df_index, fcst_inferred_df, atol=1e-4, rtol=1e-3)\n",
"pd.testing.assert_frame_equal(anom_inferred_df_index, anom_inferred_df, atol=1e-4, rtol=1e-3)\n",
"df_ds_index = df_ds_index.groupby('unique_id').tail(80)\n",
"for freq in ['Y', 'W-MON', 'Q-DEC', 'H']:\n",
" df_ds_index.index = np.concatenate(\n",
Expand All @@ -2405,7 +2409,7 @@
" fcst_inferred_df_index = nixtla_client.forecast(df_ds_index, h=10)\n",
" df_test = df_ds_index.reset_index()\n",
" fcst_inferred_df = nixtla_client.forecast(df_test, h=10)\n",
" pd.testing.assert_frame_equal(fcst_inferred_df_index, fcst_inferred_df, atol=1e-3)"
" pd.testing.assert_frame_equal(fcst_inferred_df_index, fcst_inferred_df, atol=1e-4, rtol=1e-3)"
]
},
{
Expand Down Expand Up @@ -2547,7 +2551,9 @@
"\n",
"pd.testing.assert_frame_equal(\n",
" timegpt_anomalies_df_1,\n",
" timegpt_anomalies_df_2 \n",
" timegpt_anomalies_df_2,\n",
" atol=1e-4,\n",
" rtol=1e-3,\n",
")"
]
},
Expand Down
6 changes: 5 additions & 1 deletion nixtla/nixtla_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1093,7 +1093,6 @@ def forecast(
)
out = ufp.assign_columns(out, "TimeGPT", resp["mean"])
out = _maybe_add_intervals(out, resp["intervals"])
out = _maybe_convert_level_to_quantiles(out, quantiles)
if add_history:
in_sample_df = _parse_in_sample_output(
in_sample_output=in_sample_resp,
Expand All @@ -1105,6 +1104,7 @@ def forecast(
)
in_sample_df = ufp.drop_columns(in_sample_df, target_col)
out = ufp.vertical_concat([in_sample_df, out])
out = _maybe_convert_level_to_quantiles(out, quantiles)
self._maybe_assign_feature_contributions(
expected_contributions=feature_contributions,
resp=resp,
Expand All @@ -1118,10 +1118,14 @@ def forecast(
)
if sort_idxs is not None:
out = ufp.take_rows(out, sort_idxs)
out = ufp.drop_index_if_pandas(out)
if hasattr(self, "feature_contributions"):
self.feature_contributions = ufp.take_rows(
self.feature_contributions, sort_idxs
)
self.feature_contributions = ufp.drop_index_if_pandas(
self.feature_contributions
)
out = _maybe_drop_id(df=out, id_col=id_col, drop=drop_id)
self._maybe_assign_weights(weights=resp["weights_x"], df=df, x_cols=x_cols)
return out
Expand Down