Testing and Reporting framework for Time Series Analysis
Project description
KRISI
(/creesee/)
Evaluation and Reporting Framework for Time Series Forecasting
Check Examples ~
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Krisi is a Scoring library for Time-Series Forecasting. It calculates, stores and vizualises the performance of your predictions!
Krisi is tailored to measure performance over time (metrics over time). It is from the ground-up extensible and lightweight and comes with fundamental metrics for regression, classification and residual diagnostics.
It can generate reports in:
- static PDF (with
plotly
) - interactive web (HTML) (with
plotly
) - pretty formatted for console (with
rich
andplotext
) - each figure displayed or saved as an svg
Krisi solves the following problems
- Missing Rolling window based evaluation.
→ Krisi supports evaluating metrics over time. - Most TS libraries attach reporting to modelling (eg.: Darts, Statsmodel).
→ Krisi is independent of any modeling method or library. - Extendability is tedious: only works by subclassing objects.
→ Krisi supports easy configuration of custom metrics along with an extensive library of predefined metrics. - Too many dependencies.
→ Krisi has few hard dependencies (only core libaries, eg.: sklearn and plotting libraries). - Visualisation results are too basic.
→ With Krisi you can decide to share and interactive web, a static PDF, each metric diagram displayed inline or quickly look at the ScoreCard pretty printed to the console.
Installation
The project was entirely built in python
.
Prerequisites
python >= 3.8
andpip
Then run:
pip install krisi
If you'd like to also use interactive plotting (html) and pdf generation then run:
pip install krisi "krisi[plotting]"
Quickstart
You can quickly evaluate your predictions by running:
import numpy as np
from krisi.evaluate import score
print(score(y=np.random.rand(100), predictions=np.random.rand(100)))
Outputs:
Model_20230505-15094466243320 <- you can add your name here
Mean Absolute Error 0.115402
Mean Absolute Percentage Error 3.272862
Symmetric Mean Absolute Percentage Error 0.718754
Mean Squared Error 0.020945
Root Mean Squared Error 0.144723
R-squared -1.108832
Mean of the Residuals -0.002094
Standard Deviation of the Residuals 0.144781
Krisi's main object is the ScoreCard
that contains predefined Metric
s and which you can add further Metric
s to.
import numpy as np
from krisi import score
sc = score(
y=np.random.random(1000),
predictions=np.random.random(1000),
).print()
Outputs:
┏━ Result of Model_20230505-130632d68aefea on Dataset_20230505-130632d4167ca4 tested on outofsam━┓
┃ ┃
┃ Targets and Predictions Analysis ┃
┃ ╭─────────────────┬────────────────────────────────────────────────┬────────────┬────────────╮ ┃
┃ │ Series Type │ Histogram │ Types │ Indicies │ ┃
┃ ├─────────────────┼────────────────────────────────────────────────┼────────────┼────────────┤ ┃
┃ │ Targets │ ┌────────────────────────────────────────┐ │ NaNs: 0 │ Start: 0 │ ┃
┃ │ │ 75.0┤ ██ │ │ dtype: │ End: 999 │ ┃
┃ │ │ 50.0┤ ██████████████ │ │ float64 │ │ ┃
┃ │ │ 25.0┤ ██ █████████████████████████ │ │ │ │ ┃
┃ │ │ 0.0┤███████████████████████████████████ ████│ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬────────┬─────────┬┘ │ │ │ ┃
┃ │ │ -0.30 -0.14 0.02 0.18 0.34 │ │ │ ┃
┃ ├─────────────────┼────────────────────────────────────────────────┼────────────┼────────────┤ ┃
┃ │ Predictions │ ┌────────────────────────────────────────┐ │ NaNs: 0 │ Start: 0 │ ┃
┃ │ │ 68.0┤ █ ██ │ │ dtype: │ End: 999 │ ┃
┃ │ │ 45.3┤ ███████████████ │ │ float64 │ │ ┃
┃ │ │ 22.7┤ ███████████████████████ │ │ │ │ ┃
┃ │ │ 0.0┤███████████████████████████████████ █ ██│ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬────────┬─────────┬┘ │ │ │ ┃
┃ │ │ -0.29 -0.13 0.03 0.19 0.34 │ │ │ ┃
┃ ╰─────────────────┴────────────────────────────────────────────────┴────────────┴────────────╯ ┃
┃ Residual Diagnostics ┃
┃ ╭─────────────────────┬─────────────────────────────────────────────────┬────────────────────╮ ┃
┃ │ Metric Name │ Result │ Parameters │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Residuals │ 0 0.055378 │ {} │ ┃
┃ │ (residuals) │ 1 -0.077456 │ │ ┃
┃ │ │ 2 -0.102910 │ │ ┃
┃ │ │ 3 -0.088878 │ │ ┃
┃ │ │ 4 -0.137035 │ │ ┃
┃ │ │ ... │ │ ┃
┃ │ │ 995 0.153345 │ │ ┃
┃ │ │ 996 0.222105 │ │ ┃
┃ │ │ 997 0.022042 │ │ ┃
┃ │ │ 998 0.013997 │ │ ┃
┃ │ │ 999 0.068374 │ │ ┃
┃ │ │ Length: 1000, dtype: float64 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Ljung Box │ lb_stat lb_pvalue │ {} │ ┃
┃ │ Statistics │ 1 0.410131 0.521903 │ │ ┃
┃ │ (ljung_box_statist… │ 2 0.411774 0.813925 │ │ ┃
┃ │ │ 3 0.541798 0.909617 │ │ ┃
┃ │ │ 4 4.200716 0.379523 │ │ ┃
┃ │ │ 5 4.217347 0.518566 │ │ ┃
┃ │ │ 6 5.934770 0.430537 │ │ ┃
┃ │ │ 7 9.905078 0.194017 │ │ ┃
┃ │ │ 8 10.020619 0.263582 │ │ ┃
┃ │ │ 9 11.102783 0.268729 │ │ ┃
┃ │ │ 10 11.268537 0.336983 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Mean of the │ -0.002 │ {} │ ┃
┃ │ Residuals │ │ │ ┃
┃ │ (residuals_mean) │ │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Standard Deviation │ 0.145 │ {} │ ┃
┃ │ of the Residuals │ │ │ ┃
┃ │ (residuals_std) │ │ │ ┃
┃ ╰─────────────────────┴─────────────────────────────────────────────────┴────────────────────╯ ┃
┃ Forecast Errors - Regression ┃
┃ ╭─────────────────────┬─────────────────────────────────────────────────┬────────────────────╮ ┃
┃ │ Mean Absolute Error │ 0.117 │ {} │ ┃
┃ │ (mae) │ │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Mean Absolute │ 7.322 │ {} │ ┃
┃ │ Percentage Error │ │ │ ┃
┃ │ (mape) │ │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Symmetric Mean │ 0.727 │ {} │ ┃
┃ │ Absolute Percentage │ │ │ ┃
┃ │ Error (smape) │ │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Mean Squared Error │ 0.021 │ {'squared': True} │ ┃
┃ │ (mse) │ │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Root Mean Squared │ 0.145 │ {'squared': False} │ ┃
┃ │ Error (rmse) │ │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Root Mean Squared │ 'Mean Squared Logarithmic Error cannot be used │ {'squared': False} │ ┃
┃ │ Log Error (rmsle) │ when targets contain negative values.' │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ R-squared (r_two) │ -0.982 │ {} │ ┃
┃ ╰─────────────────────┴─────────────────────────────────────────────────┴────────────────────╯ ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Evaluating Metric
s over time (on a rolling basis).
import numpy as np
from krisi import score
score(
y=np.random.rand(10000),
predictions=np.random.rand(10000),
calculation="rolling",
).print()
Outputs:
┏━ Result of Model_20230505-1303447c37e983 on Dataset_20230505-130344cea0de16 tested on outofsam━┓
┃ ┃
┃ Targets and Predictions Analysis ┃
┃ ╭─────────────────┬────────────────────────────────────────────────┬────────────┬────────────╮ ┃
┃ │ Series Type │ Histogram │ Types │ Indicies │ ┃
┃ ├─────────────────┼────────────────────────────────────────────────┼────────────┼────────────┤ ┃
┃ │ Targets │ ┌────────────────────────────────────────┐ │ NaNs: 0 │ Start: 0 │ ┃
┃ │ │ 14.0┤ ██ │ │ dtype: │ End: 249 │ ┃
┃ │ │ 11.7┤ ██ █ │ │ float64 │ │ ┃
┃ │ │ 9.3┤ ████ ██ █ ██ ██ │ │ │ │ ┃
┃ │ │ 7.0┤██ ████████████ █ █ ███████ ██ ██ ████ │ │ │ │ ┃
┃ │ │ 4.7┤███████████████████ ████████████████████│ │ │ │ ┃
┃ │ │ 2.3┤████████████████████████████████████████│ │ │ │ ┃
┃ │ │ 0.0┤████████████████████████████████████████│ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬────────┬─────────┬┘ │ │ │ ┃
┃ │ │ -0.00 0.25 0.50 0.75 1.00 │ │ │ ┃
┃ ├─────────────────┼────────────────────────────────────────────────┼────────────┼────────────┤ ┃
┃ │ Predictions │ ┌────────────────────────────────────────┐ │ NaNs: 0 │ Start: 0 │ ┃
┃ │ │ 11.0┤ ██ │ │ dtype: │ End: 249 │ ┃
┃ │ │ 9.2┤ ███ ██ █ █ ██│ │ float64 │ │ ┃
┃ │ │ 7.3┤ ██ ██████████ ████ █ ██ ███ ████│ │ │ │ ┃
┃ │ │ 5.5┤ ██████████████ ████ █████████████████│ │ │ │ ┃
┃ │ │ 3.7┤█████████████████ ████ █████████████████│ │ │ │ ┃
┃ │ │ 1.8┤████████████████████████████████████████│ │ │ │ ┃
┃ │ │ 0.0┤████████████████████████████████████████│ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬────────┬─────────┬┘ │ │ │ ┃
┃ │ │ -0.01 0.25 0.50 0.76 1.01 │ │ │ ┃
┃ ╰─────────────────┴────────────────────────────────────────────────┴────────────┴────────────╯ ┃
┃ Residual Diagnostics ┃
┃ ╭─────────────────────┬─────────────────────────────────────────────────┬────────────────────╮ ┃
┃ │ Metric Name │ Result │ Parameters │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Residuals │ 0 -0.224918 │ {} │ ┃
┃ │ (residuals) │ 1 0.250975 │ │ ┃
┃ │ │ 2 0.206893 │ │ ┃
┃ │ │ 3 0.632068 │ │ ┃
┃ │ │ 4 0.467366 │ │ ┃
┃ │ │ ... │ │ ┃
┃ │ │ 245 0.336682 │ │ ┃
┃ │ │ 246 0.132184 │ │ ┃
┃ │ │ 247 -0.339346 │ │ ┃
┃ │ │ 248 0.422431 │ │ ┃
┃ │ │ 249 -0.424224 │ │ ┃
┃ │ │ Length: 250, dtype: float64 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Ljung Box │ 'zero-size array to reduction operation maximum │ {} │ ┃
┃ │ Statistics │ which has no identity' │ │ ┃
┃ │ (ljung_box_statist… │ │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Mean of the │ ┌────────────────────────────────────────┐ │ {} │ ┃
┃ │ Residuals │ 0.73┤ ▐ ▗ │ │ │ ┃
┃ │ (residuals_mean) │ 0.51┤▐▗▌ ▐ ▖ ▗▌ ▗ ▖ █ │ │ │ ┃
┃ │ │ 0.28┤▐▐▌▗▌▟▗▟▚▗▐▚▄▌▗▐▐▙▗▞▟ ▄▌ █ ▗ ▟▗▟▌▌▙ ▄▜ ▖│ │ │ ┃
┃ │ │ 0.05┤█▐▙▐▙█▟▐▝█▌ █▌▟▜▟▛█▌█▟█▙▌█▌█▐▐▐▌▜██▐▐▐▌▙│ │ │ ┃
┃ │ │ -0.18┤█▐█▌███▐ ▜▌ ▝▜▜ █▘█▌█▜▝█▙▛███ ▘▌▐█▜▐▐▐▐▌│ │ │ ┃
┃ │ │ -0.41┤ ▘ ▘█▜▜▝ ▐▌ ▌ ▌▘▝ █▜▌▜▌█ ▌▝▛▝█▐▐ ▘│ │ │ ┃
┃ │ │ -0.63┤ ▜ ▘ ▌ ▘ ▝ ▘ ▘▝ ▌ ▝▝▐ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬────────┬─────────┬┘ │ │ ┃
┃ │ │ 1 32 63 94 125 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Standard Deviation │ ┌─────────────────────────────────────────┐ │ {} │ ┃
┃ │ of the Residuals │ 0.70┤ ▖▌ ▖ ▖ │ │ │ ┃
┃ │ (residuals_std) │ 0.58┤ ▗ ▌▌ ▗▌ ▖ ▖ █ ▖ ▌ ▟ ▗▗ │ │ │ ┃
┃ │ │ 0.47┤ ▐▗ ▌▌▗█▌ ▌ ▐▌ ▗ ▖█ ▐▙ ▌ ▖█ ▐▐ │ │ │ ┃
┃ │ │ 0.35┤ ▐▐▗ ▌▌▞█▙▗▌ ▗▚▐▌ ▐▗▖▌█ ▟▐ ▌▐▌█ ▗▐▐▌▖▗▌▞│ │ │ ┃
┃ │ │ 0.23┤ ▟▐█▌▖▌▙▌██▌▙▖▐▐▟▌ ▟█▌▌█▟█▝▟▌▐▐▜▙▜▛▌▜▌▐▌▌│ │ │ ┃
┃ │ │ 0.12┤▐▐▟█▜▚▌▌▌▛▛▌▜▐▐▝▛▌▟██▚▙▛█▌ ▀▌█▐▐▜▐▌ ▀█▌▌│ │ │ ┃
┃ │ │ 0.00┤▟ ▘▀ ▝▌▘▘▌▘ ▘ ▐▜▝▘ ▘ ▝ ▝ ▐▝ ▘ ▜▝▘│ │ │ ┃
┃ │ │ └┬─────────┬─────────┬─────────┬─────────┬┘ │ │ ┃
┃ │ │ 1 32 63 94 125 │ │ ┃
┃ ╰─────────────────────┴─────────────────────────────────────────────────┴────────────────────╯ ┃
┃ Forecast Errors - Regression ┃
┃ ╭─────────────────────┬─────────────────────────────────────────────────┬────────────────────╮ ┃
┃ │ Mean Absolute Error │ ┌─────────────────────────────────────────┐ │ {} │ ┃
┃ │ (mae) │ 0.73┤ ▗▌▖ ▖ │ │ │ ┃
┃ │ │ 0.61┤▗ ▟▐▌▌ ▗▌ ▗ ▗ ▛▖ ▌▖ ▗▌ ▗▗ ▗█ │ │ │ ┃
┃ │ │ 0.49┤▐▐▖ █▐▌▌ ▟▌ ▌ ▐▜▐▗ ▖▌▌▞▌▐█▌ █▌▄▐▐▟██ │ │ │ ┃
┃ │ │ 0.38┤▐▟█▄█▐▐▙▟█▚▙▌▗ ▄▐▐▐▐ ▖▌▌▌▌▜██▙▌███▐▐█▛▛▖▞│ │ │ ┃
┃ │ │ 0.26┤▟▝▜▐▜▌▐▌▘█▐▌▚█▐▝▀▐▐▞▜▌▙▌█▌▝██▜▐▜▐▜▀▀▜▌ █▌│ │ │ ┃
┃ │ │ 0.14┤ ▐ ▘ ▘ █ ▘ ▜▐ ▐█ ▐█▝▌█▘ ▐ ▐▐▐ ▘ █ │ │ │ ┃
┃ │ │ 0.02┤ ▝ ▌ ▀ ▝▜ ▝ ▝ ▝ ▝ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬─────────┬─────────┬┘ │ │ ┃
┃ │ │ 1 32 63 94 125 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Mean Absolute │ ┌─────────────────────────────────────────┐ │ {} │ ┃
┃ │ Percentage Error │ 38.2┤ ▗▌ │ │ │ ┃
┃ │ (mape) │ 31.8┤ ▐▌ │ │ │ ┃
┃ │ │ 25.5┤ ▟▟ ▐▌ │ │ │ ┃
┃ │ │ 19.1┤ ██ ▖ ▐▌ │ │ │ ┃
┃ │ │ 12.7┤ ██ ▐▌ ▌ ▐▌ │ │ │ ┃
┃ │ │ 6.4┤ ▗██ ▐▌ ▌ ▗ ▗ ▐▌ ▖ ▖ │ │ │ ┃
┃ │ │ 0.0┤▄▟▄▀▛▛▞█▙▄▙▄▄▄▄▄▟▚▟▟▟▄▚▙█▛▄▙█▚▟▟▚▟▄▟▜▞▞▄▞│ │ │ ┃
┃ │ │ └┬─────────┬─────────┬─────────┬─────────┬┘ │ │ ┃
┃ │ │ 1 32 63 94 125 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Symmetric Mean │ ┌─────────────────────────────────────────┐ │ {} │ ┃
┃ │ Absolute Percentage │ 0.90┤ ▟▗▌ │ │ │ ┃
┃ │ Error (smape) │ 0.75┤ █▐▌▌ ▖ ▐ ▟ ▗▌ ▗ │ │ │ ┃
┃ │ │ 0.60┤▗▐ █▌▌▌▄▗▌▖ ▗▗▐▗█ █ ▖ █▌▟▟▄ ▐ ▙ ▗│ │ │ ┃
┃ │ │ 0.46┤▐▐▙▜█▌▐█▛▟▌▌▖ ▟▐▜▐▐█ ▌▛▄█▌▌█▌███▙▐▐▐▟▛▖▌│ │ │ ┃
┃ │ │ 0.31┤▐▜█▝█▌ ▀ █▚▙▙▗▐▝▘▐▐▌▐█▌▌█▌▝███▐██▜▐▞█▌▘▛▌│ │ │ ┃
┃ │ │ 0.16┤▟ ▝ ▌▘ ▛ ▘▀▛▟ ▐█▌▐█▝▌█▌ ▝▜▜▐▐▝ ▘▘▜▌ ▘ │ │ │ ┃
┃ │ │ 0.02┤ ▌ ▝▝ ▝ ▝ ▝ ▝ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬─────────┬─────────┬┘ │ │ ┃
┃ │ │ 1 32 63 94 125 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Mean Squared Error │ ┌────────────────────────────────────────┐ │ {'squared': True} │ ┃
┃ │ (mse) │ 0.548┤ ▐▗▌ │ │ │ ┃
┃ │ │ 0.457┤ ▐▟▌ ▗▌ ▟ ▌ ▗ │ │ │ ┃
┃ │ │ 0.366┤ ▟▐█▌ ▟ ▙ ▖ ▐▚ ▟ █ ▐▌ ▄▄▗█ │ │ │ ┃
┃ │ │ 0.274┤▐▐▖ █▐█▌▗▌▌▗▌ ▛▖▌ ▗ ▐▐▄▜ █▟▗▐▌▙███▐█ │ │ │ ┃
┃ │ │ 0.183┤▐▟▙ █▐▜▌▐▌▌█▌▖▐ ▌▌▌▌▟▐▐▐█ ▙███▐▌████▐█ │ │ │ ┃
┃ │ │ 0.092┤▐▝▌██▞▐▝█▌█▝█▌▐▜▌▌▙██▐▟▐█ ▛███▐▙█▌▀▝▞▘▙▛│ │ │ ┃
┃ │ │ 0.000┤▀ ▜ ▘▝ ▝▌▝ ▘▐▞ ▛▌ ▀▞▝▝▛ ▝▌▝▛▛▌ █ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬────────┬─────────┬┘ │ │ ┃
┃ │ │ 1 32 63 94 125 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Root Mean Squared │ ┌─────────────────────────────────────────┐ │ {'squared': False} │ ┃
┃ │ Error (rmse) │ 0.74┤ ▗▌▌ ▄ ▖ ▖ │ │ │ ┃
┃ │ │ 0.62┤▗ ▟▐▌▌ ▗▌ ▖ ▐▖▗ ▛▖ ▌ ▌▖ ▟▌▄▗▗▄▗█ │ │ │ ┃
┃ │ │ 0.50┤▐▐▖ █▐▌▌ ▟▌▗▌ ▖▐▐▐▗ ▖▖▌▌▞▚▐█▙▌███▐▐███ │ │ │ ┃
┃ │ │ 0.38┤▐▀█▄█▞▐▛▞█▚▛▌▟▐▚▟▐▐▐▟▌▌▌█▌▐███▌███▞▟▜▛▛▖▞│ │ │ ┃
┃ │ │ 0.26┤▟ ▜▐▀▌▐▌▘█▝▌▚▜▐▝▝▐▐▀▜▌▙▌█▌▝██▝▐▜▐▜▘ ▝▌ █▌│ │ │ ┃
┃ │ │ 0.14┤ ▐ ▘ ▛ ▐▐ ▐█ ▐█ ▘█ ▐ ▐▐▐ █ │ │ │ ┃
┃ │ │ 0.02┤ ▝ ▌ ▀ ▝▜ ▝ ▝ ▝ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬─────────┬─────────┬┘ │ │ ┃
┃ │ │ 1 32 63 94 125 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ Root Mean Squared │ ┌────────────────────────────────────────┐ │ {'squared': False} │ ┃
┃ │ Log Error (rmsle) │ 0.533┤ ▐ ▖ │ │ │ ┃
┃ │ │ 0.447┤ ▟▐▟▌ ▗ ▖ ▖ ▐▌ ▗ ▟▗ ▗▌ ▗ ▟ │ │ │ ┃
┃ │ │ 0.361┤▐▐▖ █▐█▌ ▌▌ ▖ ▛▖▌▖ ▐▐▌█ ██▟▐▌▙▙██▐█ │ │ │ ┃
┃ │ │ 0.275┤▐▟▙▖█▞▐▜▟▌▙█▌▖▐▖▌▌▌▙█▐▐▐█ ▙███▐▌████▐▛▖▗│ │ │ ┃
┃ │ │ 0.189┤▐ ▌██▌▐ ▜▌▜▝█▌▐▜▘▌▙▀█▐▟▐█ ▛███▐▙█▌▘▜▞ ▙▌│ │ │ ┃
┃ │ │ 0.102┤▀ █ ▘▝ ▐▌ ▐▞ █▌ █▌▜▐▛ ▐▌▝█▌▘ ▛ │ │ │ ┃
┃ │ │ 0.016┤ ▝ ▝▌ ▝▘ ▀▌ ▘ ▘ ▘ ▘ │ │ │ ┃
┃ │ │ └┬─────────┬─────────┬────────┬─────────┬┘ │ │ ┃
┃ │ │ 1 32 63 94 125 │ │ ┃
┃ ├─────────────────────┼─────────────────────────────────────────────────┼────────────────────┤ ┃
┃ │ R-squared (r_two) │ ┌───────────────────────────────────┐ │ {} │ ┃
┃ │ │ 1.0┤▝▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▀▜▛▀▀▀│ │ │ ┃
┃ │ │ -242903.5┤ ▐▌ │ │ │ ┃
┃ │ │ -485808.0┤ ▐▌ │ │ │ ┃
┃ │ │ -728712.5┤ ▐▌ │ │ │ ┃
┃ │ │ -971617.0┤ ▐▌ │ │ │ ┃
┃ │ │ -1214521.4┤ ▐▌ │ │ │ ┃
┃ │ │ -1457425.9┤ ▝▌ │ │ │ ┃
┃ │ │ └┬────────┬───────┬────────┬───────┬┘ │ │ ┃
┃ │ │ 1 32 63 94 125 │ │ ┃
┃ ╰─────────────────────┴─────────────────────────────────────────────────┴────────────────────╯ ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
You can also show the results in a Report easily:
import numpy as np
from krisi import score
score(
y=np.random.rand(10000),
predictions=np.random.rand(10000),
calculation="rolling",
).generate_report('pdf')
Generates:
Default Metrics
See evaluate/library
for source.
Contributors are continously adding new default metrics, press watch to keep track of the project and see in issues planned default metrics.
Residual Diagnostics
- Mean of the Residuals
- Standard Deviation of the Residuals
- Ljung Box Statistics
- (wip) Autocorrelation of Residuals
Regression Errors
- Mean Absolute Error
- Mean Absolute Percentage Error
- Symmetric Mean Absolute Percentage Error
- Mean Squared Error
- Root Mean Squared Error
- Root Mean Squared Log Error
- R-squared
Classification Errors
- Matthew Correlation Coefficient
- F1 Score
- Precision
- Recall
- Accuracy
Examples, Walkthroughs and Blog Posts
Name | Type | Dataset Type | Docs Link | Colab |
---|---|---|---|---|
⚡️ Core Walkthrough | Walkthrough | Synthetic | Notebook | Colab |
🚄 Collapsed Metrics vs Metrics over Time | Walkthrough | Energy | - | Colab |
📚 Example Collection | Example | Synthetic | Collection Link | - |
Our Open-core Time Series Toolkit
If you want to try them out, we'd love to hear about your use case and help, please book a free 30-min call with us!
Advanced usage
Creating Metric
s with metadata.
import numpy as np
from krisi.evaluate import Metric, MetricCategories, ScoreCard
# Random targets and predictions for Demo
target, predictions = np.random.rand(100), np.random.rand(100)
# Create ScoreCard
sc = ScoreCard(target, predictions)
# Calculate a random metric for Demo
calculated_metric_example = (target - predictions).mean()
# Adding a simple new metric (a float)
# As a Dictionary:
sc["metric_barebones"] = calculated_metric_example
# As an Object assignment:
sc.another_metric_barebones = calculated_metric_example * 2.0
sc["metric_with_metadata"] = Metric(
name="A new, own Metric",
category=MetricCategories.residual,
result=calculated_metric_example * 3.0,
parameters={"hyper_1": 5.0},
)
# Updating the metadata of an existing metric
sc.metric_barebones = dict(info="Giving description to a metric")
# Print a pretty summary to the console
sc.print(with_info=True)
Outputs:
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Result of <your_model_name> on <your_dataset_name> tested on insample ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ ┃
┃ Residual Diagnostics ┃
┃ ╭─────────────────────┬────────────────────────────────────────────────┬────────────────────┬─────────────────────────────────╮ ┃
┃ │ Metric Name │ Result │ Parameters │ Info │ ┃
┃ ├─────────────────────┼────────────────────────────────────────────────┼────────────────────┼─────────────────────────────────┤ ┃
┃ │ Mean of the │ 0.035 │ {} │ '' │ ┃
┃ │ Residuals │ │ │ │ ┃
┃ │ (residuals_mean) │ │ │ │ ┃
┃ ├─────────────────────┼────────────────────────────────────────────────┼────────────────────┼─────────────────────────────────┤ ┃
┃ │ Standard Deviation │ 0.42 │ {} │ '' │ ┃
┃ │ of the Residuals │ │ │ │ ┃
┃ │ (residuals_std) │ │ │ │ ┃
┃ ├─────────────────────┼────────────────────────────────────────────────┼────────────────────┼─────────────────────────────────┤ ┃
┃ │ A new, own Metric │ 0.105 │ {'hyper_1': 5.0} │ 'Giving description to a │ ┃
┃ │ (yet_another_metri… │ │ │ metric' │ ┃
┃ ╰─────────────────────┴────────────────────────────────────────────────┴────────────────────┴─────────────────────────────────╯ ┃
┃ Forecast Errors - Regression ┃
┃ ╭─────────────────────┬────────────────────────────────────────────────┬────────────────────┬─────────────────────────────────╮ ┃
┃ │ Mean Absolute Error │ 0.35 │ {} │ '(Mean absolute error) │ ┃
┃ │ (mae) │ │ │ represents the difference │ ┃
┃ │ │ │ │ between the original and │ ┃
┃ │ │ │ │ predicted values extracted by │ ┃
┃ │ │ │ │ averaged the absolute │ ┃
┃ │ │ │ │ difference over the data set.' │ ┃
┃ ├─────────────────────┼────────────────────────────────────────────────┼────────────────────┼─────────────────────────────────┤ ┃
┃ │ Mean Absolute │ 2.543 │ {} │ '' │ ┃
┃ │ Percentage Error │ │ │ │ ┃
┃ │ (mape) │ │ │ │ ┃
┃ ├─────────────────────┼────────────────────────────────────────────────┼────────────────────┼─────────────────────────────────┤ ┃
┃ │ Mean Squared Error │ 0.178 │ {'squared': True} │ '(Mean Squared Error) │ ┃
┃ │ (mse) │ │ │ represents the difference │ ┃
┃ │ │ │ │ between the original and │ ┃
┃ │ │ │ │ predicted values extracted by │ ┃
┃ │ │ │ │ squared the average difference │ ┃
┃ │ │ │ │ over the data set.' │ ┃
┃ ├─────────────────────┼────────────────────────────────────────────────┼────────────────────┼─────────────────────────────────┤ ┃
┃ │ Root Mean Squared │ 0.421 │ {'squared': False} │ '(Root Mean Squared Error) is │ ┃
┃ │ Error (rmse) │ │ │ the error rate by the square │ ┃
┃ │ │ │ │ root of Mean Squared Error.' │ ┃
┃ ├─────────────────────┼────────────────────────────────────────────────┼────────────────────┼─────────────────────────────────┤ ┃
┃ │ Root Mean Squared │ 0.29 │ {'squared': False} │ '' │ ┃
┃ │ Log Error (rmsle) │ │ │ │ ┃
┃ ├─────────────────────┼────────────────────────────────────────────────┼────────────────────┼─────────────────────────────────┤ ┃
┃ │ R-squared (r2) │ -1.28 │ {} │ '(Coefficient of determination) │ ┃
┃ │ │ │ │ represents the coefficient of │ ┃
┃ │ │ │ │ how well the values fit │ ┃
┃ │ │ │ │ compared to the original │ ┃
┃ │ │ │ │ values. The value from 0 to 1 │ ┃
┃ │ │ │ │ interpreted as percentages. The │ ┃
┃ │ │ │ │ higher the value is, the better │ ┃
┃ │ │ │ │ the model is.' │ ┃
┃ ╰─────────────────────┴────────────────────────────────────────────────┴────────────────────┴─────────────────────────────────╯ ┃
┃ Unknown ┃
┃ ╭─────────────────────┬────────────────────────────────────────────────┬────────────────────┬─────────────────────────────────╮ ┃
┃ │ own_metric │ 0.035 │ {} │ '' │ ┃
┃ │ (own_metric) │ │ │ │ ┃
┃ ├─────────────────────┼────────────────────────────────────────────────┼────────────────────┼─────────────────────────────────┤ ┃
┃ │ another_metric │ 0.07 │ {} │ '' │ ┃
┃ │ (another_metric) │ │ │ │ ┃
┃ ╰─────────────────────┴────────────────────────────────────────────────┴────────────────────┴─────────────────────────────────╯ ┃
┃ ┃
┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛
Contribution
Join our Discord for live discussion!
Submit an issue or reach out to us on info at dream-faster.ai for any inquiries.
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