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‎.gitignore

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__pycache__
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*.egg-info
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build
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dist

‎.pre-commit-config.yaml

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rev: 20.8b1
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hooks:
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- id: black
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- repo: https://gitlab.com/pycqa/flake8
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- repo: https://github.com/PyCQA/flake8
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rev: 3.9.0
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hooks:
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- id: flake8

‎ml_matrics/elements.py

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from matplotlib.cm import get_cmap
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from matplotlib.colors import LogNorm, Normalize
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from matplotlib.patches import Rectangle
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from pymatgen import Composition
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from pymatgen.core import Composition
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from ml_matrics.utils import ROOT, annotate_bar_heights
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‎readme.md

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[![Tests](https://github.com/janosh/ml-matrics/workflows/Tests/badge.svg)](https://github.com/janosh/ml-matrics/actions)
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[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/janosh/ml-matrics/main.svg)](https://results.pre-commit.ci/latest/github/janosh/ml-matrics/main)
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[![PyPI](https://img.shields.io/pypi/v/ml-matrics)](https://pypi.org/project/ml-matrics)
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[![This project supports Python 3.8+](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://python.org/downloads)
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[![License](https://img.shields.io/github/license/janosh/ml-matrics?label=License)](/license)
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[![GitHub Repo Size](https://img.shields.io/github/repo-size/janosh/ml-matrics?label=Repo+Size)](https://github.com/janosh/ml-matrics/graphs/contributors)
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[![This project supports Python 3.8+](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://python.org/downloads)
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</h4>
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## Installation
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```sh
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pip install -U git+https://github.com/janosh/ml-matrics
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pip install ml-matrics
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```
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For a locally editable install, use
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git clone https://github.com/janosh/ml-matrics && pip install -e ml-matrics
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```
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To specify a dependence on this package in `requirements.txt`, use
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```txt
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pandas==1.1.2
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numpy==1.20.1
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git+git://github.com/janosh/ml-matrics
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```
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To specify a specific branch or commit, append its name or hash, e.g.
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```txt
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git+git://github.com/janosh/ml-matrics@main # default
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git+git://github.com/janosh/ml-matrics@41b95ec
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```
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## Parity Plots
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See [`ml_matrics/parity.py`](ml_matrics/parity.py).
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| [`density_scatter(xs, ys, ...)`](ml_matrics/parity.py) | [`density_scatter_with_hist(xs, ys, ...)`](ml_matrics/parity.py) |
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| :---------------------------------------------------------------: | :----------------------------------------------------------------: |
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| ![density_scatter](assets/density_scatter.svg) | ![density_scatter_with_hist](assets/density_scatter_with_hist.svg) |
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| [`density_hexbin(xs, ys, ...)`](ml_matrics/parity.py) | [`density_hexbin_with_hist(xs, ys, ...)`](ml_matrics/parity.py) |
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| ![density_hexbin](assets/density_hexbin.svg) | ![density_hexbin_with_hist](assets/density_hexbin_with_hist.svg) |
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| [`scatter_with_err_bar(xs, ys, yerr, ...)`](ml_matrics/parity.py) | [`residual_vs_actual(y_true, y_pred, ...)`](ml_matrics/parity.py) |
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| ![scatter_with_err_bar](assets/scatter_with_err_bar.svg) | ![residual_vs_actual](assets/residual_vs_actual.svg) |
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| [`density_scatter(xs, ys, ...)`](ml_matrics/parity.py) | [`density_scatter_with_hist(xs, ys, ...)`](ml_matrics/parity.py) |
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| :---------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |
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| ![density_scatter](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/density_scatter.svg) | ![density_scatter_with_hist](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/density_scatter_with_hist.svg) |
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| [`density_hexbin(xs, ys, ...)`](ml_matrics/parity.py) | [`density_hexbin_with_hist(xs, ys, ...)`](ml_matrics/parity.py) |
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| ![density_hexbin](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/density_hexbin.svg) | ![density_hexbin_with_hist](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/density_hexbin_with_hist.svg) |
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| [`scatter_with_err_bar(xs, ys, yerr, ...)`](ml_matrics/parity.py) | [`residual_vs_actual(y_true, y_pred, ...)`](ml_matrics/parity.py) |
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| ![scatter_with_err_bar](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/scatter_with_err_bar.svg) | ![residual_vs_actual](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/residual_vs_actual.svg) |
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## Elements
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See [`ml_matrics/elements.py`](ml_matrics/elements.py).
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| [`ptable_elemental_prevalence(compositions)`](ml_matrics/elements.py) | [`ptable_elemental_prevalence(compositions, log=True)`](ml_matrics/elements.py) |
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| :--------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: |
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| ![ptable_elemental_prevalence](assets/ptable_elemental_prevalence.svg) | ![ptable_elemental_prevalence_log](assets/ptable_elemental_prevalence_log.svg) |
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| [`hist_elemental_prevalence(compositions)`](ml_matrics/elements.py) | [`hist_elemental_prevalence(compositions, log=True, bar_values='count')`](ml_matrics/elements.py) |
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| ![hist_elemental_prevalence](assets/hist_elemental_prevalence.svg) | ![hist_elemental_prevalence_log_count](assets/hist_elemental_prevalence_log_count.svg) |
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| [`ptable_elemental_ratio(comps_a, comps_b)`](ml_matrics/elements.py) | [`ptable_elemental_ratio(comps_b, comps_a, log=True)`](ml_matrics/elements.py) |
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| ![ptable_elemental_ratio](assets/ptable_elemental_ratio.svg) | ![ptable_elemental_ratio_log](assets/ptable_elemental_ratio_log.svg) |
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| [`ptable_elemental_prevalence(compositions)`](ml_matrics/elements.py) | [`ptable_elemental_prevalence(compositions, log=True)`](ml_matrics/elements.py) |
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| :-----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------: |
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| ![ptable_elemental_prevalence](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/ptable_elemental_prevalence.svg) | ![ptable_elemental_prevalence_log](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/ptable_elemental_prevalence_log.svg) |
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| [`hist_elemental_prevalence(compositions)`](ml_matrics/elements.py) | [`hist_elemental_prevalence(compositions, log=True, bar_values='count')`](ml_matrics/elements.py) |
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| ![hist_elemental_prevalence](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/hist_elemental_prevalence.svg) | ![hist_elemental_prevalence_log_count](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/hist_elemental_prevalence_log_count.svg) |
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| [`ptable_elemental_ratio(comps_a, comps_b)`](ml_matrics/elements.py) | [`ptable_elemental_ratio(comps_b, comps_a, log=True)`](ml_matrics/elements.py) |
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| ![ptable_elemental_ratio](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/ptable_elemental_ratio.svg) | ![ptable_elemental_ratio_log](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/ptable_elemental_ratio_log.svg) |
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## Uncertainty Calibration
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See [`ml_matrics/quantile.py`](ml_matrics/quantile.py).
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| [`qq_gaussian(y_true, y_pred, y_std)`](ml_matrics/quantile.py) | [`qq_gaussian(y_true, y_pred, y_std: dict)`](ml_matrics/quantile.py) |
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| :------------------------------------------------------------: | :------------------------------------------------------------------: |
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| ![normal_prob_plot](assets/normal_prob_plot.svg) | ![normal_prob_plot_multiple](assets/normal_prob_plot_multiple.svg) |
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| [`qq_gaussian(y_true, y_pred, y_std)`](ml_matrics/quantile.py) | [`qq_gaussian(y_true, y_pred, y_std: dict)`](ml_matrics/quantile.py) |
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| :-------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------: |
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| ![normal_prob_plot](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/normal_prob_plot.svg) | ![normal_prob_plot_multiple](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/normal_prob_plot_multiple.svg) |
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## Ranking
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See [`ml_matrics/ranking.py`](ml_matrics/ranking.py).
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| [`err_decay(y_true, y_pred, y_std)`](ml_matrics/ranking.py) | [`err_decay(y_true, y_pred, y_std: dict)`](ml_matrics/ranking.py) |
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| :---------------------------------------------------------: | :---------------------------------------------------------------: |
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| ![err_decay](assets/err_decay.svg) | ![err_decay_multiple](assets/err_decay_multiple.svg) |
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| [`err_decay(y_true, y_pred, y_std)`](ml_matrics/ranking.py) | [`err_decay(y_true, y_pred, y_std: dict)`](ml_matrics/ranking.py) |
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| :-----------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------: |
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| ![err_decay](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/err_decay.svg) | ![err_decay_multiple](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/err_decay_multiple.svg) |
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## Cumulative Error and Residual
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See [`ml_matrics/cumulative.py`](ml_matrics/cumulative.py).
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| [`cum_err(preds, targets)`](ml_matrics/cumulative.py) | [`cum_res(preds, targets)`](ml_matrics/cumulative.py) |
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| :---------------------------------------------------: | :----------------------------------------------------: |
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| ![cumulative_error](assets/cumulative_error.svg) | ![cumulative_residual](assets/cumulative_residual.svg) |
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| [`cum_err(preds, targets)`](ml_matrics/cumulative.py) | [`cum_res(preds, targets)`](ml_matrics/cumulative.py) |
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| :-------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------: |
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| ![cumulative_error](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/cumulative_error.svg) | ![cumulative_residual](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/cumulative_residual.svg) |
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## Classification Metrics
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See [`ml_matrics/relevance.py`](ml_matrics/relevance.py).
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| [`roc_curve(targets, proba_pos)`](ml_matrics/relevance.py) | [`precision_recall_curve(targets, proba_pos)`](ml_matrics/relevance.py) |
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| :--------------------------------------------------------: | :---------------------------------------------------------------------: |
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| ![roc_curve](assets/roc_curve.svg) | ![precision_recall_curve](assets/precision_recall_curve.svg) |
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| [`roc_curve(targets, proba_pos)`](ml_matrics/relevance.py) | [`precision_recall_curve(targets, proba_pos)`](ml_matrics/relevance.py) |
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| :-----------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------: |
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| ![roc_curve](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/roc_curve.svg) | ![precision_recall_curve](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/precision_recall_curve.svg) |
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## Correlation
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See [`ml_matrics/correlation.py`](ml_matrics/correlation.py).
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| [`marchenko_pastur(corr_mat, gamma=ncols/nrows)`](ml_matrics/correlation.py) | [`marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows)`](ml_matrics/correlation.py) |
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| :--------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------: |
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| ![marchenko_pastur](assets/marchenko_pastur.svg) | ![marchenko_pastur_significant_eval](assets/marchenko_pastur_significant_eval.svg) |
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| [`marchenko_pastur(corr_mat, gamma=ncols/nrows)`](ml_matrics/correlation.py) | [`marchenko_pastur(corr_mat_significant_eval, gamma=ncols/nrows)`](ml_matrics/correlation.py) |
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| :-------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------: |
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| ![marchenko_pastur](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/marchenko_pastur.svg) | ![marchenko_pastur_significant_eval](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/marchenko_pastur_significant_eval.svg) |
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## Histograms
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See [`ml_matrics/histograms.py`](ml_matrics/histograms.py).
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| [`residual_hist(y_true, y_pred)`](ml_matrics/histograms.py) | [`true_pred_hist(y_true, y_pred, y_std)`](ml_matrics/histograms.py) |
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| :-----------------------------------------------------------: | :-----------------------------------------------------------------: |
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| ![residual_hist](assets/residual_hist.svg) | ![true_pred_hist](assets/true_pred_hist.svg) |
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| [`spacegroup_hist(y_true, y_pred)`](ml_matrics/histograms.py) | |
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| ![spacegroup_hist](assets/spacegroup_hist.svg) | |
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| [`residual_hist(y_true, y_pred)`](ml_matrics/histograms.py) | [`true_pred_hist(y_true, y_pred, y_std)`](ml_matrics/histograms.py) |
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| :-----------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: |
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| ![residual_hist](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/residual_hist.svg) | ![true_pred_hist](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/true_pred_hist.svg) |
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| [`spacegroup_hist(y_true, y_pred)`](ml_matrics/histograms.py) | |
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| ![spacegroup_hist](https://raw.githubusercontent.com/janosh/ml-matrics/main/assets/spacegroup_hist.svg) | |
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## Adding Assets
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‎scripts/fetch_mp_data.py

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# %%
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import pandas as pd
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from matminer.datasets import load_dataset
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from pymatgen import MPRester
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from pymatgen.ext.matproj import MPRester
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‎setup.cfg

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[metadata]
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name = ml-matrics
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version = 0.1.0
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description = Metrics and visualizations for model performance in data-driven materials discovery
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long_description = file: readme.md
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long_description_content_type = text/markdown
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url = https://github.com/janosh/ml-matrics
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author = Janosh Riebesell
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author_email = [email protected]
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license = MIT
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license_file = license
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keywords = machine-learning, materials-discovery, metrics, plots, visualizations, model-performance
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classifiers =
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Programming Language :: Python :: 3
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Programming Language :: Python :: 3 :: Only
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Programming Language :: Python :: 3.6
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Programming Language :: Python :: 3.7
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Programming Language :: Python :: 3.8
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Programming Language :: Python :: 3.9
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License :: OSI Approved :: MIT License
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[options]
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packages = find:
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package_data =
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python_requires = >=3.6
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install_requires=
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numpy >= 1.19
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pandas >= 1.0.0
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matplotlib >= 3.1
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pymatgen >= 2021.2.16
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scipy >= 1.5
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scikit_learn >= 0.24
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[options.package_data]
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* = ../data/periodic_table.csv
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[options.packages.find]
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exclude =
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tests*
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[bdist_wheel]
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universal = True
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# Tooling Config
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[flake8]
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# tell flake8 to use black's default line length
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[tool:pytest]
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testpaths = tests
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# https://stackoverflow.com/a/50821160
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addopts = -p no:warnings

‎setup.py

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from setuptools import find_namespace_packages, setup
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from setuptools import setup
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setup(
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name="ml-matrics",
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version="0.0.1",
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author="Janosh Riebesell",
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author_email="[email protected]",
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description="A collection of plots useful in data-driven materials science",
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long_description=open("readme.md").read(),
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long_description_content_type="text/markdown",
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url="https://github.com/janosh/ml-matrics",
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packages=find_namespace_packages(include=["ml_matrics*"]),
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classifiers=[
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"Programming Language :: Python :: 3.8",
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"License :: OSI Approved :: MIT License",
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"Operating System :: OS Independent",
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],
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)
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setup()

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