From c6b68457504dfc4e6ffbb67ba7f70253f781918d Mon Sep 17 00:00:00 2001 From: jgallowa07 Date: Tue, 11 Jun 2024 17:02:13 -0700 Subject: [PATCH] added plotting unit tests and ge scale ridge penalty --- multidms/biophysical.py | 19 +- multidms/model.py | 172 +-- notebooks/fit_delta_BA1_example.ipynb | 1781 ++++++++++++++----------- pyproject.toml | 7 +- tests/test_data.py | 45 +- 5 files changed, 1099 insertions(+), 925 deletions(-) diff --git a/multidms/biophysical.py b/multidms/biophysical.py index 35d852c..d322502 100644 --- a/multidms/biophysical.py +++ b/multidms/biophysical.py @@ -278,7 +278,7 @@ def softplus_activation(d_params, act, lower_bound=-3.5, hinge_scale=0.1, **kwar hinge_scale # GAMMA # * (jnp.logaddexp(0, (act - (lower_bound + d_params["gamma_d"])) / hinge_scale)) - * (jnp.logaddexp(0, act - lower_bound / hinge_scale)) + * (jnp.logaddexp(0, (act - lower_bound) / hinge_scale)) + lower_bound # GAMMA # + d_params["gamma_d"] @@ -413,6 +413,8 @@ def smooth_objective( params, data, scale_coeff_ridge_beta=0.0, + scale_coeff_ridge_ge_scale=0.0, + scale_coeff_ridge_ge_bias=0.0, huber_scale=1, **kwargs, ): @@ -432,6 +434,10 @@ def smooth_objective( Scale parameter for Huber loss function scale_coeff_ridge_beta : float Ridge penalty coefficient for shift parameters + scale_coeff_ridge_ge_scale : float + Ridge penalty coefficient for global epistasis scale parameter + scale_coeff_ridge_ge_bias : float + Ridge penalty coefficient for global epistasis bias parameter kwargs : dict Additional keyword arguments to pass to the biophysical model function @@ -474,4 +480,13 @@ def smooth_objective( huber_cost /= len(X) - return huber_cost + beta_ridge_penalty + ge_scale_ridge_penalty = ( + scale_coeff_ridge_ge_scale * (params["theta"]["ge_scale"] ** 2).sum() + ) + ge_bias_ridge_penalty = ( + scale_coeff_ridge_ge_bias * (params["theta"]["ge_bias"] ** 2).sum() + ) + + return ( + huber_cost + beta_ridge_penalty + ge_scale_ridge_penalty + ge_bias_ridge_penalty + ) diff --git a/multidms/model.py b/multidms/model.py index eb19bc6..bc634d0 100644 --- a/multidms/model.py +++ b/multidms/model.py @@ -8,7 +8,7 @@ import math import warnings -from functools import lru_cache, partial, reduce, cached_property +from functools import lru_cache, partial, cached_property from frozendict import frozendict from multidms import Data @@ -267,7 +267,10 @@ def __init__( ) elif epistatic_model == multidms.biophysical.identity_activation: - self._scaled_data_params["theta"] = dict(ghost_param=jnp.zeros(shape=(1,))) + self._scaled_data_params["theta"] = dict( + ge_scale=jnp.zeros(shape=(1,)), + ge_bias=jnp.zeros(shape=(1,)), + ) elif epistatic_model == multidms.biophysical.nn_global_epistasis: if n_hidden_units is None: @@ -484,7 +487,7 @@ def get_variants_df(self, phenotype_as_effect=True): based on the current state of the model. """ # this is what well update and return - variants_df = self._data.variants_df.copy() + variants_df = self.data.variants_df.copy() # initialize new columns for pheno in ["latent", "func_score"]: @@ -871,20 +874,28 @@ def add_phenotypes_to_df( return ret - def mutation_site_summary_df(self, agg_func=onp.mean, times_seen_threshold=0): + def mutation_site_summary_df(self, agg_func="mean", **kwargs): """ Get all single mutational attributes from self._data updated with all model specific attributes, then aggregate - all numerical columns by "sites" using - ``agg`` function. The mean values are given by default. + all numerical columns by "sites" + + Parameters + ---------- + agg_func : str + Aggregation function to use on the numerical columns. + Defaults to "mean". + **kwargs + Additional keyword arguments to pass to get_mutations_df. + + Returns + ------- + pandas.DataFrame + A summary of the mutation attributes aggregated by site. """ numerics = ["int16", "int32", "int64", "float16", "float32", "float64"] - mut_df = self.mutations_df.select_dtypes(include=numerics) - times_seen_cols = [c for c in mut_df.columns if "times" in c] - for c in times_seen_cols: - mut_df = mut_df[mut_df[c] >= times_seen_threshold] - - return mut_df.groupby("sites").aggregate(agg_func) + mut_df = self.get_mutations_df(**kwargs).select_dtypes(include=numerics) + return mut_df.groupby("sites").agg(agg_func) def get_condition_params(self, condition=None): """Get the relent parameters for a model prediction""" @@ -1193,7 +1204,7 @@ def plot_pred_accuracy( between model predicted functional score of all variants in the training with ground truth measurements. """ - df = self.variants_df + df = self.get_variants_df(phenotype_as_effect=False) df = df.assign( is_wt=df["aa_substitutions"].apply( @@ -1204,7 +1215,9 @@ def plot_pred_accuracy( if ax is None: fig, ax = plt.subplots(figsize=[3, 3]) - func_score = "corrected_func_score" if self.gamma_corrected else "func_score" + # GAMMA + # func_score = "corrected_func_score" if self.gamma_corrected else "func_score" + func_score = "func_score" sns.scatterplot( data=df.sample(frac=1), x="predicted_func_score", @@ -1240,13 +1253,14 @@ def plot_pred_accuracy( c=self._data.condition_colors[c], ) start_y += -0.05 - ax.set_ylabel("functional score") + # ax.set_ylabel("functional score") ax.set_xlabel("predicted functional score") ax.axhline(0, color="k", ls="--", lw=2) ax.axvline(0, color="k", ls="--", lw=2) - ax.set_ylabel("functional score + gamma$_{d}$") + # ax.set_ylabel("functional score + gamma$_{d}$") + ax.set_ylabel("measured functional score") plt.tight_layout() if saveas: fig.savefig(saveas) @@ -1262,7 +1276,7 @@ def plot_epistasis( gamma corrected ground truth measurements of all samples in the training set. """ - df = self.variants_df + df = self.get_variants_df(phenotype_as_effect=False) df = df.assign( is_wt=df["aa_substitutions"].apply( @@ -1273,7 +1287,9 @@ def plot_epistasis( if ax is None: fig, ax = plt.subplots(figsize=[3, 3]) - func_score = "corrected_func_score" if self.gamma_corrected else "func_score" + # GAMMA + # func_score = "corrected_func_score" if self.gamma_corrected else "func_score" + func_score = "func_score" sns.scatterplot( data=df.sample(frac=sample), x="predicted_latent", @@ -1305,7 +1321,7 @@ def plot_epistasis( ax.axhline(0, color="k", ls="--", lw=2) ax.set_xlim([xlb, xub]) ax.set_ylim([ylb, yub]) - ax.set_ylabel("functional score") + ax.set_ylabel("measured functional score") ax.set_xlabel("predicted latent phenotype") plt.tight_layout() @@ -1316,10 +1332,10 @@ def plot_epistasis( return ax def plot_param_hist( - self, param, show=True, saveas=False, times_seen_threshold=3, ax=None, **kwargs + self, param, show=True, saveas=False, times_seen_threshold=0, ax=None, **kwargs ): """Plot the histogram of a parameter.""" - mut_effects_df = self.mutations_df + mut_effects_df = self.get_mutations_df() if ax is None: fig, ax = plt.subplots(figsize=[3, 3]) @@ -1372,18 +1388,18 @@ def plot_param_hist( return ax def plot_param_heatmap( - self, param, show=True, saveas=False, times_seen_threshold=3, ax=None, **kwargs + self, param, show=True, saveas=False, times_seen_threshold=0, ax=None, **kwargs ): """ Plot the heatmap of a parameters associated with specific sites and substitutions. """ - if not param.startswith("beta") and not param.startswith("S"): + if not param.startswith("beta") and not param.startswith("shift"): raise ValueError( "Parameter to visualize must be an existing beta, or shift parameter" ) - mut_effects_df = self.mutations_df + mut_effects_df = self.get_mutations_df() if ax is None: fig, ax = plt.subplots(figsize=[12, 3]) @@ -1419,8 +1435,8 @@ def plot_shifts_by_site( condition, show=True, saveas=False, - times_seen_threshold=3, - agg_func=onp.mean, + times_seen_threshold=0, + agg_func="mean", ax=None, **kwargs, ): @@ -1470,65 +1486,6 @@ def plot_shifts_by_site( plt.show() return ax - def plot_fit_param_comp_scatter( - self, - other, - self_param="beta", - other_param="beta", - figsize=[5, 4], - saveas=None, - show=True, - site_agg_func=None, - ): - """Plot a scatter plot of the parameter values of two models""" - if not site_agg_func: - dfs = [self.mutations_df, other.mutations_df] - else: - dfs = [ - self.mutation_site_summary_df(agg=site_agg_func).reset_index(), - other.mutation_site_summary_df(agg=site_agg_func).reset_index(), - ] - - combine_on = "mutation" if site_agg_func is None else "sites" - comb_mut_effects = reduce( - lambda l, r: pd.merge(l, r, how="inner", on=combine_on), # noqa: E741 - dfs, - ) - comb_mut_effects["is_stop"] = [ - True if "*" in s else False for s in comb_mut_effects[combine_on] - ] - - same = self_param == other_param - x = f"{self_param}_x" if same else self_param - y = f"{other_param}_y" if same else other_param - - fig, ax = plt.subplots(figsize=figsize) - r = pearsonr(comb_mut_effects[x], comb_mut_effects[y])[0] - sns.scatterplot( - data=comb_mut_effects, - x=x, - y=y, - hue="is_stop", - alpha=0.6, - palette="deep", - ax=ax, - ) - - xlb, xub = [-1, 1] + onp.quantile(comb_mut_effects[x], [0.00, 1.0]) - ylb, yub = [-1, 1] + onp.quantile(comb_mut_effects[y], [0.00, 1.0]) - min1 = min(xlb, ylb) - max1 = max(xub, yub) - ax.plot([min1, max1], [min1, max1], ls="--", c="k") - ax.annotate(f"$r = {r:.2f}$", (0.7, 0.1), xycoords="axes fraction", fontsize=12) - plt.tight_layout() - - if saveas: - fig.saveas(saveas) - if show: - plt.show() - - return fig, ax - def mut_param_heatmap( self, mut_param="shift", @@ -1548,7 +1505,13 @@ def mut_param_heatmap( muts_df = self.get_mutations_df( times_seen_threshold=times_seen_threshold, phenotype_as_effect=phenotype_as_effect, - return_split=False, + return_split=True, + ).rename( + columns={ + "wts": "wildtype", + "muts": "mutant", + "sites": "site", + } ) # drop columns which are not the mutational parameter of interest @@ -1558,9 +1521,9 @@ def mut_param_heatmap( muts_df.drop(drop_cols, axis=1, inplace=True) # add in the mutation annotations - muts_df["wildtype"], muts_df["site"], muts_df["mutant"] = zip( - *muts_df.reset_index()["mutation"].map(self.data.parse_mut) - ) + # muts_df["wildtype"], muts_df["site"], muts_df["mutant"] = zip( + # *muts_df.reset_index()["mutation"].map(self.data.parse_mut) + # ) # no longer need mutation annotation muts_df.reset_index(drop=True, inplace=True) @@ -1597,22 +1560,21 @@ def mut_param_heatmap( # melt conditions and stats cols, beta is already "tall" # note that we must rename conditions with "." in the # name to "_" to avoid altair errors - if mut_param == "beta": - muts_df_tall = muts_df.assign(condition=reference.replace(".", "_")) - else: - muts_df_tall = muts_df.melt( - id_vars=["wildtype", "site", "mutant"] + addtl_tooltip_stats, - value_vars=[c for c in muts_df.columns if c.startswith(mut_param)], - var_name="condition", - value_name=mut_param, - ) - muts_df_tall.condition.replace( - { - f"{mut_param}_{condition}": condition.replace(".", "_") - for condition in conditions - }, - inplace=True, - ) + # if mut_param == f"beta_{reference}": + # muts_df_tall = muts_df.assign(condition=reference.replace(".", "_")) + # else: + muts_df_tall = muts_df.melt( + id_vars=["wildtype", "site", "mutant"] + addtl_tooltip_stats, + value_vars=[c for c in muts_df.columns if c.startswith(mut_param)], + var_name="condition", + value_name=mut_param, + ) + muts_df_tall["condition"] = muts_df_tall.condition.replace( + { + f"{mut_param}_{condition}": condition.replace(".", "_") + for condition in conditions + }, + ) # add in condition colors, rename for altair condition_colors = { diff --git a/notebooks/fit_delta_BA1_example.ipynb b/notebooks/fit_delta_BA1_example.ipynb index f10e88b..ffa6874 100644 --- a/notebooks/fit_delta_BA1_example.ipynb +++ b/notebooks/fit_delta_BA1_example.ipynb @@ -37,13 +37,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "7ca0b6ca", "metadata": {}, "outputs": [], "source": [ - "%matplotlib inline\n", - "warnings.simplefilter('ignore')" + "# %matplotlib inline\n", + "# warnings.simplefilter('ignore')" ] }, { @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "edbdcb12", "metadata": {}, "outputs": [ @@ -209,7 +209,7 @@ "[10000 rows x 3 columns]" ] }, - "execution_count": 4, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -230,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "bceb25a0", "metadata": {}, "outputs": [ @@ -252,7 +252,7 @@ "Name: count, Length: 7637, dtype: int64" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -279,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "8bfc72bd", "metadata": { "scrolled": true @@ -295,7 +295,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a043385c1bc542259e87ec632d034aa7", + "model_id": "7b86638463a349bc8f9ebfa7209745ff", "version_major": 2, "version_minor": 0 }, @@ -316,7 +316,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4dfcd1c11e374482aaacfa9fa8b5116e", + "model_id": "dc41e597b3204d6691f46fa1f1cb33e0", "version_major": 2, "version_minor": 0 }, @@ -338,7 +338,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "098402973c2d4a6195b4a108f0051d70", + "model_id": "b39263b1a2fb4daea8b9a02ec7e97937", "version_major": 2, "version_minor": 0 }, @@ -349,6 +349,14 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -360,7 +368,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ff86a00c18af4ef9938f0fe50b42b677", + "model_id": "c26d9611fd4d419791efb2be83191afe", "version_major": 2, "version_minor": 0 }, @@ -371,6 +379,14 @@ "metadata": {}, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -384,7 +400,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68d98557ad1e4c6c83af0f7defe794db", + "model_id": "51c42f505186418da5a708523328d32c", "version_major": 2, "version_minor": 0 }, @@ -394,6 +410,14 @@ }, "metadata": {}, "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n" + ] } ], "source": [ @@ -427,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "dd493152", "metadata": {}, "outputs": [ @@ -495,7 +519,7 @@ "5 L L" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -515,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "861db643", "metadata": {}, "outputs": [ @@ -583,7 +607,7 @@ "339 G D" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -603,7 +627,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "c4e343b8", "metadata": {}, "outputs": [ @@ -613,7 +637,7 @@ "('M1I', 'M1-', 'F2L', 'F2Y', 'V3F')" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -633,7 +657,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "10448926", "metadata": {}, "outputs": [ @@ -673,8 +697,8 @@ " M\n", " 1\n", " I\n", - " 0.0\n", - " 1.0\n", + " 0\n", + " 1\n", " \n", " \n", " 1\n", @@ -682,8 +706,8 @@ " M\n", " 1\n", " -\n", - " 1.0\n", - " 0.0\n", + " 1\n", + " 0\n", " \n", " \n", " 2\n", @@ -691,8 +715,8 @@ " F\n", " 2\n", " L\n", - " 1.0\n", - " 1.0\n", + " 1\n", + " 1\n", " \n", " \n", " 3\n", @@ -700,8 +724,8 @@ " F\n", " 2\n", " Y\n", - " 1.0\n", - " 0.0\n", + " 1\n", + " 0\n", " \n", " \n", " 4\n", @@ -709,8 +733,8 @@ " V\n", " 3\n", " F\n", - " 1.0\n", - " 4.0\n", + " 1\n", + " 4\n", " \n", " \n", "\n", @@ -718,14 +742,14 @@ ], "text/plain": [ " mutation wts sites muts times_seen_Delta-2 times_seen_Omicron_BA1-2\n", - "0 M1I M 1 I 0.0 1.0\n", - "1 M1- M 1 - 1.0 0.0\n", - "2 F2L F 2 L 1.0 1.0\n", - "3 F2Y F 2 Y 1.0 0.0\n", - "4 V3F V 3 F 1.0 4.0" + "0 M1I M 1 I 0 1\n", + "1 M1- M 1 - 1 0\n", + "2 F2L F 2 L 1 1\n", + "3 F2Y F 2 Y 1 0\n", + "4 V3F V 3 F 1 4" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -745,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "c4e9d392", "metadata": {}, "outputs": [ @@ -831,7 +855,7 @@ "4 Delta-2 A1020C 1 0.50800 A1020C" ] }, - "execution_count": 11, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -851,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "652d1683", "metadata": {}, "outputs": [ @@ -914,7 +938,7 @@ "6831 R19T A67V T95I G156E G339D S373P N440K G446S G... " ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -967,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "25e8e80a", "metadata": {}, "outputs": [], @@ -975,6 +999,28 @@ "model = multidms.Model(data)" ] }, + { + "cell_type": "code", + "execution_count": 13, + "id": "26bc7602", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model\n", + "Name: unnamed\n", + "Data: unnamed\n", + "Converged: False\n", + "\n" + ] + } + ], + "source": [ + "print(model)" + ] + }, { "cell_type": "markdown", "id": "42a25703", @@ -986,11 +1032,20 @@ { "cell_type": "code", "execution_count": 14, - "id": "f24570e7", + "id": "946bf04d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgallowa/Projects/multidms/multidms/model.py:1181: RuntimeWarning: Model training error did not reach the tolerance threshold. Final error: 0.021850742189881294, tolerance: 0.0001\n", + " warnings.warn(\n" + ] + } + ], "source": [ - "model.fit(maxiter=15000)" + "model.fit(maxiter=5000)" ] }, { @@ -998,14 +1053,78 @@ "id": "542e01a0", "metadata": {}, "source": [ - "This method uses the [jaxopt.ProximalGradient](https://jaxopt.github.io/stable/_autosummary/jaxopt.ProximalGradient.html) optimizer to fit the parameters (in place) to the data. Note that later we'll introduce the `model_collection` module interface for a more streamlined approach to creating and fitting one or more `Model` objects -- but the attributes and methods of individual `Model` objects are still be quite useful. \n", - "\n", - "For example, the `Model` object allows provides many of the same properties, like mutations and variants dataframes, but add additional features relevant to the parameters of this model. `Model.get_mutations_df` returns the associated data object's mutations_df as seen above, along with the $\\beta$ and $S_{m,h}$ parameter's associated with each mutation. " + "This method uses the [jaxopt.ProximalGradient](https://jaxopt.github.io/stable/_autosummary/jaxopt.ProximalGradient.html) optimizer to fit the parameters (in place) to the data. Note that later we'll introduce the `model_collection` module interface for a more streamlined approach to creating and fitting one or more `Model` objects -- but the attributes and methods of individual `Model` objects are still be quite useful.\n" + ] + }, + { + "cell_type": "markdown", + "id": "d85063b4", + "metadata": {}, + "source": [ + "Note the warning about convergence. The default convergence threshold is set to $10^{-4}$ which can sometimes take more playing with hyperparameters and more iterations to acheive. Toi suppress this warning, simply pass `warn_unconverged=True`. For our spike analysis manuscript, we needed to regularize model parameters and train the models for close to 30K iterations before convergence at this tolerence was acheived." + ] + }, + { + "cell_type": "markdown", + "id": "596d97c9", + "metadata": {}, + "source": [ + "`Model.convergence_trajectory_df` gives the models error metric (as reported by [jaxopt state object](https://jaxopt.github.io/stable/unconstrained.html#unpacking-results) -- and is useful to see how the model is changing through the fitting process) as well as the loss on the training data." ] }, { "cell_type": "code", "execution_count": 15, + "id": "56be955f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import seaborn as sns\n", + "fig, ax = plt.subplots(2, 1, figsize=(10, 5))\n", + "for i, m in enumerate([\"loss\", \"error\"]):\n", + " sns.lineplot(\n", + " model.convergence_trajectory_df,\n", + " x=\"step\",\n", + " y=m,\n", + " ax=ax[i],\n", + " # yscale=\"log\",\n", + " )\n", + " ax[i].set_yscale(\"log\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "512fa68a", + "metadata": {}, + "source": [ + "Here, we can see the model did not converge, but the model loss seems to be roughly unchanged at this point and is good enough for the purposes of this example." + ] + }, + { + "cell_type": "markdown", + "id": "d33aeb10", + "metadata": {}, + "source": [ + "\n", + "The `Model` object allows provides many of the same properties, like mutations and variants dataframes, but add additional features relevant to the parameters of this model. `Model.get_mutations_df` returns the associated data object's mutations_df as seen above, along with the $\\beta$ and $S_{m,h}$ parameter's associated with each mutation. " + ] + }, + { + "cell_type": "code", + "execution_count": 16, "id": "fa1f0e0e-a060-4393-9bf0-27f3bdd4283c", "metadata": {}, "outputs": [ @@ -1013,22 +1132,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "Help on function get_mutations_df in module multidms.model:\n", + "Help on _lru_cache_wrapper in module multidms.model:\n", "\n", - "get_mutations_df(self, phenotype_as_effect=True, times_seen_threshold=0, return_split=True)\n", - " Mutation attributes and phenotypic effects.\n", + "get_mutations_df(self, times_seen_threshold=0, phenotype_as_effect=True, return_split=True)\n", + " Mutation attributes and phenotypic effects\n", + " based on the current state of the model.\n", " \n", " Parameters\n", " ----------\n", - " phenotype_as_effect : bool, optional\n", - " if True, phenotypes (both latent, and func_score)\n", - " are calculated as the _difference_ between predicted\n", - " phenotype of a given variant and the respective experimental\n", - " wildtype prediction. Otherwise, report the unmodified\n", - " model prediction.\n", " times_seen_threshold : int, optional\n", " Only report mutations that have been seen at least\n", " this many times in each condition. Defaults to 0.\n", + " phenotype_as_effect : bool, optional\n", + " if True, phenotypes are reported as the difference\n", + " from the conditional wildtype prediction. Otherwise,\n", + " report the unmodified model prediction.\n", " return_split : bool, optional\n", " If True, return the split mutations as separate columns:\n", " 'wts', 'sites', and 'muts'.\n", @@ -1040,9 +1158,13 @@ " A copy of the mutations data, `self.data.mutations_df`,\n", " with the mutations column set as the index, and columns\n", " with the mutational attributes (e.g. betas, shifts) and\n", - " conditional phenotypes (e.g. func_scores) added.\n", - " Phenotypes are predicted\n", - " based on the current state of the model.\n", + " conditional functional score effect (e.g. ) added.\n", + " \n", + " The columns are ordered as follows:\n", + " - beta_a, beta_b, ... : the latent effect of the mutation\n", + " - shift_b, shift_c, ... : the conditional shift of the mutation\n", + " - predicted_func_score_a, predicted_func_score_b, ... : the\n", + " predicted functional score of the mutation.\n", "\n" ] } @@ -1053,7 +1175,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "e72a9a79", "metadata": {}, "outputs": [ @@ -1078,15 +1200,16 @@ " \n", " \n", " \n", - " beta\n", - " shift_Omicron_BA1-2\n", - " predicted_func_score_Delta-2\n", - " predicted_func_score_Omicron_BA1-2\n", - " times_seen_Delta-2\n", - " times_seen_Omicron_BA1-2\n", " wts\n", " sites\n", " muts\n", + " times_seen_Delta-2\n", + " times_seen_Omicron_BA1-2\n", + " beta_Delta-2\n", + " beta_Omicron_BA1-2\n", + " shift_Omicron_BA1-2\n", + " predicted_func_score_Delta-2\n", + " predicted_func_score_Omicron_BA1-2\n", " \n", " \n", " mutation\n", @@ -1099,100 +1222,106 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " M1I\n", - " -4.897398\n", - " -0.009559\n", - " -3.227453\n", - " -3.229870\n", - " 0.0\n", - " 1.0\n", " M\n", " 1\n", " I\n", + " 0\n", + " 1\n", + " -0.670376\n", + " -0.670376\n", + " 0.000000\n", + " -1.924031\n", + " -2.092655\n", " \n", " \n", " M1-\n", - " -15.076238\n", - " 0.000000\n", - " -3.498683\n", - " -3.498683\n", - " 1.0\n", - " 0.0\n", " M\n", " 1\n", " -\n", + " 1\n", + " 0\n", + " -1.210833\n", + " -1.217279\n", + " -0.006446\n", + " -3.518891\n", + " -3.696349\n", " \n", " \n", " F2L\n", - " 6.499686\n", - " -0.002973\n", - " 0.760441\n", - " 0.760439\n", - " 1.0\n", - " 1.0\n", " F\n", " 2\n", " L\n", + " 1\n", + " 1\n", + " 0.376333\n", + " 0.376333\n", + " 0.000000\n", + " 0.886560\n", + " 0.760452\n", " \n", " \n", " F2Y\n", - " 7.830030\n", - " 0.000000\n", - " 0.760958\n", - " 0.760958\n", - " 1.0\n", - " 0.0\n", " F\n", " 2\n", " Y\n", + " 1\n", + " 0\n", + " 0.624053\n", + " 0.624053\n", + " 0.000000\n", + " 1.397594\n", + " 1.287470\n", " \n", " \n", " V3F\n", - " 0.289656\n", - " -1.952068\n", - " 0.437663\n", - " -0.800541\n", - " 1.0\n", - " 4.0\n", " V\n", " 3\n", " F\n", + " 1\n", + " 4\n", + " 0.061322\n", + " -0.366089\n", + " -0.427411\n", + " 0.137714\n", + " -1.191158\n", " \n", " \n", "\n", "" ], "text/plain": [ - " beta shift_Omicron_BA1-2 predicted_func_score_Delta-2 \\\n", + " wts sites muts times_seen_Delta-2 times_seen_Omicron_BA1-2 \\\n", "mutation \n", - "M1I -4.897398 -0.009559 -3.227453 \n", - "M1- -15.076238 0.000000 -3.498683 \n", - "F2L 6.499686 -0.002973 0.760441 \n", - "F2Y 7.830030 0.000000 0.760958 \n", - "V3F 0.289656 -1.952068 0.437663 \n", - "\n", - " predicted_func_score_Omicron_BA1-2 times_seen_Delta-2 \\\n", - "mutation \n", - "M1I -3.229870 0.0 \n", - "M1- -3.498683 1.0 \n", - "F2L 0.760439 1.0 \n", - "F2Y 0.760958 1.0 \n", - "V3F -0.800541 1.0 \n", - "\n", - " times_seen_Omicron_BA1-2 wts sites muts \n", - "mutation \n", - "M1I 1.0 M 1 I \n", - "M1- 0.0 M 1 - \n", - "F2L 1.0 F 2 L \n", - "F2Y 0.0 F 2 Y \n", - "V3F 4.0 V 3 F " + "M1I M 1 I 0 1 \n", + "M1- M 1 - 1 0 \n", + "F2L F 2 L 1 1 \n", + "F2Y F 2 Y 1 0 \n", + "V3F V 3 F 1 4 \n", + "\n", + " beta_Delta-2 beta_Omicron_BA1-2 shift_Omicron_BA1-2 \\\n", + "mutation \n", + "M1I -0.670376 -0.670376 0.000000 \n", + "M1- -1.210833 -1.217279 -0.006446 \n", + "F2L 0.376333 0.376333 0.000000 \n", + "F2Y 0.624053 0.624053 0.000000 \n", + "V3F 0.061322 -0.366089 -0.427411 \n", + "\n", + " predicted_func_score_Delta-2 predicted_func_score_Omicron_BA1-2 \n", + "mutation \n", + "M1I -1.924031 -2.092655 \n", + "M1- -3.518891 -3.696349 \n", + "F2L 0.886560 0.760452 \n", + "F2Y 1.397594 1.287470 \n", + "V3F 0.137714 -1.191158 " ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1212,7 +1341,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "996c3954-3429-4f9e-92f8-927bbc211f4f", "metadata": {}, "outputs": [ @@ -1220,7 +1349,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Help on function get_variants_df in module multidms.model:\n", + "Help on _lru_cache_wrapper in module multidms.model:\n", "\n", "get_variants_df(self, phenotype_as_effect=True)\n", " Training data with model predictions for latent,\n", @@ -1251,7 +1380,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "c9101e39", "metadata": {}, "outputs": [ @@ -1293,8 +1422,8 @@ " 599\n", " -0.15963\n", " \n", - " 2.209213\n", - " 0.339735\n", + " 0.000000\n", + " -3.712308e-16\n", " \n", " \n", " 1\n", @@ -1303,8 +1432,8 @@ " 1\n", " -1.29760\n", " A1016S\n", - " 0.090485\n", - " -1.272478\n", + " -0.456546\n", + " -1.270584e+00\n", " \n", " \n", " 2\n", @@ -1313,8 +1442,8 @@ " 1\n", " -0.88240\n", " A1016T\n", - " 0.598338\n", - " -0.749924\n", + " -0.213680\n", + " -5.759269e-01\n", " \n", " \n", " 3\n", @@ -1323,8 +1452,8 @@ " 1\n", " -0.03900\n", " A1016T K1191L\n", - " 1.250767\n", - " -0.186957\n", + " -0.126598\n", + " -3.365835e-01\n", " \n", " \n", " 4\n", @@ -1333,8 +1462,8 @@ " 1\n", " 0.50800\n", " A1020C\n", - " 3.341482\n", - " 0.615564\n", + " 0.261282\n", + " 6.454972e-01\n", " \n", " \n", "\n", @@ -1349,20 +1478,41 @@ "4 Delta-2 A1020C 1 0.50800 A1020C \n", "\n", " predicted_latent predicted_func_score \n", - "0 2.209213 0.339735 \n", - "1 0.090485 -1.272478 \n", - "2 0.598338 -0.749924 \n", - "3 1.250767 -0.186957 \n", - "4 3.341482 0.615564 " + "0 0.000000 -3.712308e-16 \n", + "1 -0.456546 -1.270584e+00 \n", + "2 -0.213680 -5.759269e-01 \n", + "3 -0.126598 -3.365835e-01 \n", + "4 0.261282 6.454972e-01 " ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "model.get_variants_df(phenotype_as_effect=False).head()" + "model.get_variants_df().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "796d537a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ProxGradState(iter_num=Array(5000, dtype=int64, weak_type=True), stepsize=Array(0.5, dtype=float64), error=Array(0.02185074, dtype=float64), aux=None, velocity={'beta': {'Delta-2': Array([-0.67035874, -1.21096323, 0.37641966, ..., -0.63904707,\n", + " 0.09749028, 0.60156794], dtype=float64), 'Omicron_BA1-2': Array([-0.67035874, -1.21740883, 0.37641966, ..., 0.32806356,\n", + " -0.56374356, 0.60156794], dtype=float64)}, 'beta0': {'Delta-2': Array([0.75801103], dtype=float64), 'Omicron_BA1-2': Array([0.70161721], dtype=float64)}, 'shift': {'Delta-2': Array([0., 0., 0., ..., 0., 0., 0.], dtype=float64), 'Omicron_BA1-2': Array([ 0. , -0.00644561, 0. , ..., 0.96711063,\n", + " -0.66123384, 0. ], dtype=float64)}, 'theta': {'ge_bias': Array([-8.17273411], dtype=float64), 'ge_scale': Array([11.973], dtype=float64)}}, t=Array(2502.95282886, dtype=float64, weak_type=True))\n" + ] + } + ], + "source": [ + "print(model.state)" ] }, { @@ -1397,13 +1547,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "id": "fc8f6c11", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -1421,95 +1571,242 @@ "plt.show()" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0345a4c6", - "metadata": {}, - "source": [ - "We can also take a quick look at the distribution of any parameter set in the model. Below we'll take a look at the distribution of shift parameters for the non reference BA1 condition. The distribution, by default, splits the shifts associated with stop codon mutations as a sanity check for the model fit. We expect stop codons to be equally deleterious no matter which condition they occur in, and thus, they should primarily be zero." - ] - }, { "cell_type": "code", - "execution_count": 20, - "id": "8228b5ff", + "execution_count": 22, + "id": "c0fcec26", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" + "{'shift': {'Delta-2': array([0., 0., 0., ..., 0., 0., 0.]),\n", + " 'Omicron_BA1-2': array([ 0. , -0.00644573, 0. , ..., 0.96713367,\n", + " -0.66127992, -0. ])},\n", + " 'theta': {'ge_bias': Array([-8.17271859], dtype=float64),\n", + " 'ge_scale': Array([11.973], dtype=float64)},\n", + " 'beta': {'Delta-2': Array([-0.67037649, -1.21083294, 0.37633261, ..., -0.63905219,\n", + " 0.09754517, 0.60171278], dtype=float64),\n", + " 'Omicron_BA1-2': Array([-0.67037649, -1.21727867, 0.37633261, ..., 0.32808149,\n", + " -0.56373475, 0.60171278], dtype=float64)},\n", + " 'beta0': {'Delta-2': Array([0.75802565], dtype=float64),\n", + " 'Omicron_BA1-2': Array([-1.15687509], dtype=float64)}}" ] }, + "execution_count": 22, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "fig, ax = plt.subplots(figsize=[8,4])\n", - "agg_func = lambda x: onp.abs(onp.mean(onp.sum(x)))\n", - "model.plot_param_hist(\"shift_Omicron_BA1-2\", ax=ax, show=False)\n", - "ax.set_yscale(\"log\")\n", - "ax.legend()\n", - "ax.set_ylabel(\"log value\")\n", - "ax.set_title(\"Shift parameter value distribution\")\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "855ad82b", - "metadata": {}, - "source": [ - "Perhaps the best way to explore parameter values associated with individual mutations, is `Model.mut_shift_plot()` which offers the ability to interactively visualize a model's _beta_ ($\\beta_m$), experimental _shift_ ($\\Delta_{d,m}$), and _phenotype_ predictions ($\\hat{y}_{m, d}$). The plot is interactive, and allows you to hover over a mutation to see the associated values. The plot also allows you to zoom in on a region of interest using the site zoom bar. " + "model.params" ] }, { "cell_type": "code", "execution_count": 23, - "id": "3adf1b46", + "id": "e8e38c95", "metadata": {}, "outputs": [ { "data": { "text/html": [ + "
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\n", "" ], "text/plain": [ "alt.VConcatChart(...)" ] }, - "execution_count": 25, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1753,7 +2050,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "id": "1fa1aae5", "metadata": {}, "outputs": [], @@ -1801,7 +2098,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 33, "id": "71863ff4", "metadata": {}, "outputs": [ @@ -1886,7 +2183,7 @@ " 432316\n", " -0.7932\n", " G614K Q762E Q1071R\n", - " Omicron_BA.2\n", + " Omicron_BA2\n", " 2\n", " 3\n", " \n", @@ -1894,7 +2191,7 @@ " 432317\n", " -0.3706\n", " D339T\n", - " Omicron_BA.2\n", + " Omicron_BA2\n", " 2\n", " 1\n", " \n", @@ -1902,7 +2199,7 @@ " 432318\n", " -0.6116\n", " I358L T1006I T1066S T1077A\n", - " Omicron_BA.2\n", + " Omicron_BA2\n", " 2\n", " 4\n", " \n", @@ -1910,7 +2207,7 @@ " 432319\n", " -0.4363\n", " S408R R765L K1073E\n", - " Omicron_BA.2\n", + " Omicron_BA2\n", " 2\n", " 3\n", " \n", @@ -1918,7 +2215,7 @@ " 432320\n", " -3.5000\n", " S98A A570M D1163Y S1252C\n", - " Omicron_BA.2\n", + " Omicron_BA2\n", " 2\n", " 4\n", " \n", @@ -1928,18 +2225,18 @@ "
" ], "text/plain": [ - " func_score aa_substitutions condition replicate \\\n", - "0 -0.5087 L24V F486L D820E Delta 1 \n", - "1 -0.1940 N1125K Delta 1 \n", - "2 0.9906 V16I D138C F456Y T678S E990D Delta 1 \n", - "3 -0.6554 G75S T76I M731I L1004F Delta 1 \n", - "4 -3.5000 L176S L229P K558R S975Y T998S Delta 1 \n", - "... ... ... ... ... \n", - "432316 -0.7932 G614K Q762E Q1071R Omicron_BA.2 2 \n", - "432317 -0.3706 D339T Omicron_BA.2 2 \n", - "432318 -0.6116 I358L T1006I T1066S T1077A Omicron_BA.2 2 \n", - "432319 -0.4363 S408R R765L K1073E Omicron_BA.2 2 \n", - "432320 -3.5000 S98A A570M D1163Y S1252C Omicron_BA.2 2 \n", + " func_score aa_substitutions condition replicate \\\n", + "0 -0.5087 L24V F486L D820E Delta 1 \n", + "1 -0.1940 N1125K Delta 1 \n", + "2 0.9906 V16I D138C F456Y T678S E990D Delta 1 \n", + "3 -0.6554 G75S T76I M731I L1004F Delta 1 \n", + "4 -3.5000 L176S L229P K558R S975Y T998S Delta 1 \n", + "... ... ... ... ... \n", + "432316 -0.7932 G614K Q762E Q1071R Omicron_BA2 2 \n", + "432317 -0.3706 D339T Omicron_BA2 2 \n", + "432318 -0.6116 I358L T1006I T1066S T1077A Omicron_BA2 2 \n", + "432319 -0.4363 S408R R765L K1073E Omicron_BA2 2 \n", + "432320 -3.5000 S98A A570M D1163Y S1252C Omicron_BA2 2 \n", "\n", " n_subs \n", "0 3 \n", @@ -1957,19 +2254,23 @@ "[432321 rows x 5 columns]" ] }, - "execution_count": 27, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "func_score_df = pd.read_csv(\"docs_func_score_df_delta_BA1_BA2.csv\").fillna(\"\")\n", + "func_score_df = (\n", + " pd.read_csv(\"docs_func_score_df_delta_BA1_BA2.csv\")\n", + " .fillna(\"\")\n", + " .replace({\"Omicron_BA.1\" : \"Omicron_BA1\", \"Omicron_BA.2\" : \"Omicron_BA2\"})\n", + ")\n", "func_score_df" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 34, "id": "c9449c29", "metadata": {}, "outputs": [ @@ -1996,6 +2297,27 @@ "func_score_df.info()" ] }, + { + "cell_type": "code", + "execution_count": 35, + "id": "b4ebf908", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['Delta', 'Omicron_BA1', 'Omicron_BA2'], dtype=object)" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "func_score_df.condition.unique()" + ] + }, { "cell_type": "markdown", "id": "4595b256", @@ -2008,15 +2330,37 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 36, "id": "bef3c901", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n", + "/home/jgallowa/mambaforge/envs/multidms-dev/lib/python3.11/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = os.fork()\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "{1: Data(Replicate 1), 2: Data(Replicate 2)}\n" + "{1: Data, 2: Data}\n" ] } ], @@ -2047,7 +2391,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 37, "id": "544c1598", "metadata": {}, "outputs": [], @@ -2084,38 +2428,32 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 38, "id": "d68ea914", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "model Model(Model-1)\n", - "dataset_name Replicate 1\n", - "step_loss [6.707913875579834, 0.6865768432617188]\n", - "epistatic_model Sigmoid\n", - "output_activation Identity\n", - "scale_coeff_lasso_shift 0.00001\n", - "scale_coeff_ridge_beta 0\n", - "scale_coeff_ridge_shift 0\n", - "scale_coeff_ridge_gamma 0\n", - "scale_coeff_ridge_alpha_d 0.001\n", - "huber_scale_huber 1\n", - "gamma_corrected False\n", - "alpha_d True\n", - "init_beta_naught 0.0\n", - "lock_beta_naught_at None\n", - "tol 0.0001\n", - "num_training_steps 1\n", - "iterations_per_step 15000\n", - "n_hidden_units 5\n", - "lower_bound None\n", - "PRNGKey 0\n", + "epistatic_model Sigmoid\n", + "output_activation Identity\n", + "init_theta_scale 6.5\n", + "init_theta_bias -3.5\n", + "n_hidden_units 5\n", + "lower_bound None\n", + "PRNGKey 0\n", + "num_training_steps 1\n", + "iterations_per_step 15000\n", + "alpha_d True\n", + "scale_coeff_ridge_alpha_d 0.001\n", + "scale_coeff_lasso_shift 0.00001\n", + "dataset_name Replicate 1\n", + "model Model\\nName: unnamed\\nData: Replicate 1\\nConve...\n", + "fit_time 88\n", "dtype: object" ] }, - "execution_count": 31, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2135,7 +2473,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 39, "id": "475fafe3", "metadata": {}, "outputs": [ @@ -2144,23 +2482,23 @@ "text/html": [ "\n", "\n", - "
\n", + "
\n", "" ], "text/plain": [ "alt.VConcatChart(...)" ] }, - "execution_count": 32, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2234,19 +2572,18 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 45, "id": "b1b6438f", "metadata": {}, "outputs": [], "source": [ "collection_params = {\n", " \"dataset\": list(data_replicates.values()),\n", - " \"num_training_steps\" : [1],\n", - " \"iterations_per_step\": [15000],\n", + " \"maxiter\": [1000],\n", " \"output_activation\" : [\"Softplus\"],\n", " \"lower_bound\" : [-3.5],\n", - " \"alpha_d\" : [True],\n", - " \"scale_coeff_ridge_alpha_d\": [1e-3],\n", + " \"scale_coeff_ridge_beta\" : [1e-6],\n", + " \"scale_coeff_ridge_ge_scale\": [1e-3],\n", " \"scale_coeff_lasso_shift\": [0.0, 1e-6, 1e-5, 5e-5, 1e-4, 1e-3],\n", "}" ] @@ -2261,7 +2598,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 47, "id": "c0dc0e7f", "metadata": {}, "outputs": [ @@ -2269,108 +2606,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "[{'alpha_d': True,\n", - " 'dataset': Data(Replicate 1),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 0.0,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 1),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 1e-06,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 1),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 1e-05,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 1),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 5e-05,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 1),\n", - " 'iterations_per_step': 15000,\n", + "[{'dataset': Data,\n", " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 0.0001,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 1),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 0.001,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 2),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", + " 'maxiter': 1000,\n", " 'output_activation': 'Softplus',\n", " 'scale_coeff_lasso_shift': 0.0,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 2),\n", - " 'iterations_per_step': 15000,\n", + " 'scale_coeff_ridge_beta': 1e-06,\n", + " 'scale_coeff_ridge_ge_scale': 0.001},\n", + " {'dataset': Data,\n", " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", + " 'maxiter': 1000,\n", " 'output_activation': 'Softplus',\n", " 'scale_coeff_lasso_shift': 1e-06,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 2),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 1e-05,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 2),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 5e-05,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 2),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 0.0001,\n", - " 'scale_coeff_ridge_alpha_d': 0.001},\n", - " {'alpha_d': True,\n", - " 'dataset': Data(Replicate 2),\n", - " 'iterations_per_step': 15000,\n", - " 'lower_bound': -3.5,\n", - " 'num_training_steps': 1,\n", - " 'output_activation': 'Softplus',\n", - " 'scale_coeff_lasso_shift': 0.001,\n", - " 'scale_coeff_ridge_alpha_d': 0.001}]\n" + " 'scale_coeff_ridge_beta': 1e-06,\n", + " 'scale_coeff_ridge_ge_scale': 0.001}]\n" ] } ], "source": [ "from pprint import pprint\n", - "pprint(multidms.model_collection._explode_params_dict(collection_params))" + "pprint(multidms.utils.explode_params_dict(collection_params)[:2])" ] }, { @@ -2383,12 +2638,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 49, "id": "1454a6d2", "metadata": {}, "outputs": [], "source": [ - "n_fit, n_failed, fit_models = multidms.model_collection.fit_models(collection_params, n_threads=4)" + "n_fit, n_failed, fit_models = multidms.model_collection.fit_models(collection_params, n_threads=12)" ] }, { @@ -2401,7 +2656,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 50, "id": "f4c28eda", "metadata": {}, "outputs": [ @@ -2426,412 +2681,290 @@ " \n", " \n", " \n", - " model\n", - " dataset_name\n", - " step_loss\n", " epistatic_model\n", " output_activation\n", - " scale_coeff_lasso_shift\n", - " scale_coeff_ridge_beta\n", - " scale_coeff_ridge_shift\n", - " scale_coeff_ridge_gamma\n", - " scale_coeff_ridge_alpha_d\n", - " ...\n", - " gamma_corrected\n", - " alpha_d\n", - " init_beta_naught\n", - " lock_beta_naught_at\n", - " tol\n", - " num_training_steps\n", - " iterations_per_step\n", + " init_theta_scale\n", + " init_theta_bias\n", " n_hidden_units\n", " lower_bound\n", " PRNGKey\n", + " maxiter\n", + " scale_coeff_lasso_shift\n", + " scale_coeff_ridge_beta\n", + " scale_coeff_ridge_ge_scale\n", + " dataset_name\n", + " model\n", + " fit_time\n", " \n", " \n", " \n", " \n", " 0\n", - " Model(Model-0)\n", - " Replicate 1\n", - " [4.571287155151367, 0.6238023638725281]\n", " Sigmoid\n", " Softplus\n", - " 0.0\n", - " 0\n", - " 0\n", - " 0\n", - " 0.001\n", - " ...\n", - " False\n", - " True\n", - " 0.0\n", - " None\n", - " 0.0001\n", - " 1\n", - " 15000\n", + " 6.5\n", + " -3.5\n", " 5\n", " -3.5\n", " 0\n", + " 1000\n", + " 0.0\n", + " 0.000001\n", + " 0.001\n", + " Replicate 1\n", + " Model\\nName: unnamed\\nData: Replicate 1\\nConve...\n", + " 109\n", " \n", " \n", " 1\n", - " Model(Model-0)\n", - " Replicate 1\n", - " [4.571287155151367, 0.6282336115837097]\n", " Sigmoid\n", " Softplus\n", - " 0.000001\n", - " 0\n", - " 0\n", - " 0\n", - " 0.001\n", - " ...\n", - " False\n", - " True\n", - " 0.0\n", - " None\n", - " 0.0001\n", - " 1\n", - " 15000\n", + " 6.5\n", + " -3.5\n", " 5\n", " -3.5\n", " 0\n", + " 1000\n", + " 0.000001\n", + " 0.000001\n", + " 0.001\n", + " Replicate 1\n", + " Model\\nName: unnamed\\nData: Replicate 1\\nConve...\n", + " 108\n", " \n", " \n", " 2\n", - " Model(Model-0)\n", - " Replicate 1\n", - " [4.571287155151367, 0.6726276278495789]\n", " Sigmoid\n", " Softplus\n", - " 0.00001\n", - " 0\n", - " 0\n", - " 0\n", - " 0.001\n", - " ...\n", - " False\n", - " True\n", - " 0.0\n", - " None\n", - " 0.0001\n", - " 1\n", - " 15000\n", + " 6.5\n", + " -3.5\n", " 5\n", " -3.5\n", " 0\n", + " 1000\n", + " 0.00001\n", + " 0.000001\n", + " 0.001\n", + " Replicate 1\n", + " Model\\nName: unnamed\\nData: Replicate 1\\nConve...\n", + " 108\n", " \n", " \n", " 3\n", - " Model(Model-0)\n", - " Replicate 1\n", - " [4.571287155151367, 0.8288853168487549]\n", " Sigmoid\n", " Softplus\n", - " 0.00005\n", - " 0\n", - " 0\n", - " 0\n", - " 0.001\n", - " ...\n", - " False\n", - " True\n", - " 0.0\n", - " None\n", - " 0.0001\n", - " 1\n", - " 15000\n", + " 6.5\n", + " -3.5\n", " 5\n", " -3.5\n", " 0\n", + " 1000\n", + " 0.00005\n", + " 0.000001\n", + " 0.001\n", + " Replicate 1\n", + " Model\\nName: unnamed\\nData: Replicate 1\\nConve...\n", + " 108\n", " \n", " \n", " 4\n", - " Model(Model-1)\n", - " Replicate 1\n", - " [4.571287155151367, 0.8795142769813538]\n", " Sigmoid\n", " Softplus\n", - " 0.0001\n", - " 0\n", - " 0\n", - " 0\n", - " 0.001\n", - " ...\n", - " False\n", - " True\n", - " 0.0\n", - " None\n", - " 0.0001\n", - " 1\n", - " 15000\n", + " 6.5\n", + " -3.5\n", " 5\n", " -3.5\n", " 0\n", + " 1000\n", + " 0.0001\n", + " 0.000001\n", + " 0.001\n", + " Replicate 1\n", + " Model\\nName: unnamed\\nData: Replicate 1\\nConve...\n", + " 109\n", " \n", " \n", " 5\n", - " Model(Model-1)\n", - " Replicate 1\n", - " [4.571287155151367, 0.9037127494812012]\n", " Sigmoid\n", " Softplus\n", - " 0.001\n", - " 0\n", - " 0\n", - " 0\n", - " 0.001\n", - " ...\n", - " False\n", - " True\n", - " 0.0\n", - " None\n", - " 0.0001\n", - " 1\n", - " 15000\n", + " 6.5\n", + " -3.5\n", " 5\n", " -3.5\n", " 0\n", + " 1000\n", + " 0.001\n", + " 0.000001\n", + " 0.001\n", + " Replicate 1\n", + " Model\\nName: unnamed\\nData: Replicate 1\\nConve...\n", + " 107\n", " \n", " \n", " 6\n", - " Model(Model-1)\n", - " Replicate 2\n", - " [3.9341390132904053, 0.5897706747055054]\n", " Sigmoid\n", " Softplus\n", - " 0.0\n", - " 0\n", - " 0\n", - " 0\n", - " 0.001\n", - " ...\n", - " False\n", - " True\n", - " 0.0\n", - " None\n", - " 0.0001\n", - " 1\n", - " 15000\n", + " 6.5\n", + " -3.5\n", " 5\n", " -3.5\n", " 0\n", + " 1000\n", + " 0.0\n", + " 0.000001\n", + " 0.001\n", + " Replicate 2\n", + " Model\\nName: unnamed\\nData: Replicate 2\\nConve...\n", + " 106\n", " \n", " \n", " 7\n", - " Model(Model-1)\n", - " Replicate 2\n", - " [3.9341390132904053, 0.5946934819221497]\n", " Sigmoid\n", " Softplus\n", - " 0.000001\n", - " 0\n", - " 0\n", - " 0\n", - " 0.001\n", - " ...\n", - " False\n", - " True\n", - " 0.0\n", - " None\n", - " 0.0001\n", - " 1\n", - " 15000\n", + " 6.5\n", + " -3.5\n", " 5\n", " -3.5\n", " 0\n", + " 1000\n", + " 0.000001\n", + " 0.000001\n", + " 0.001\n", + " Replicate 2\n", + " Model\\nName: unnamed\\nData: Replicate 2\\nConve...\n", + " 105\n", " \n", " \n", " 8\n", - 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"

12 rows × 21 columns

\n", "" ], "text/plain": [ - " model dataset_name step_loss \\\n", - "0 Model(Model-0) Replicate 1 [4.571287155151367, 0.6238023638725281] \n", - "1 Model(Model-0) Replicate 1 [4.571287155151367, 0.6282336115837097] \n", - "2 Model(Model-0) Replicate 1 [4.571287155151367, 0.6726276278495789] \n", - "3 Model(Model-0) Replicate 1 [4.571287155151367, 0.8288853168487549] \n", - "4 Model(Model-1) Replicate 1 [4.571287155151367, 0.8795142769813538] \n", - "5 Model(Model-1) Replicate 1 [4.571287155151367, 0.9037127494812012] \n", - "6 Model(Model-1) Replicate 2 [3.9341390132904053, 0.5897706747055054] \n", - "7 Model(Model-1) Replicate 2 [3.9341390132904053, 0.5946934819221497] \n", - "8 Model(Model-2) Replicate 2 [3.9341390132904053, 0.6400377750396729] \n", - "9 Model(Model-2) Replicate 2 [3.9341390132904053, 0.7173289656639099] \n", - "10 Model(Model-2) Replicate 2 [3.9341390132904053, 0.8550326824188232] \n", - "11 Model(Model-2) Replicate 2 [3.9341390132904053, 0.881784200668335] \n", - "\n", - " epistatic_model output_activation scale_coeff_lasso_shift \\\n", - "0 Sigmoid Softplus 0.0 \n", - "1 Sigmoid Softplus 0.000001 \n", - "2 Sigmoid Softplus 0.00001 \n", - "3 Sigmoid Softplus 0.00005 \n", - "4 Sigmoid Softplus 0.0001 \n", - "5 Sigmoid Softplus 0.001 \n", - "6 Sigmoid Softplus 0.0 \n", - "7 Sigmoid Softplus 0.000001 \n", - "8 Sigmoid Softplus 0.00001 \n", - "9 Sigmoid Softplus 0.00005 \n", - "10 Sigmoid Softplus 0.0001 \n", - "11 Sigmoid Softplus 0.001 \n", - "\n", - " scale_coeff_ridge_beta scale_coeff_ridge_shift scale_coeff_ridge_gamma \\\n", - "0 0 0 0 \n", - "1 0 0 0 \n", - "2 0 0 0 \n", - "3 0 0 0 \n", - "4 0 0 0 \n", - "5 0 0 0 \n", - "6 0 0 0 \n", - "7 0 0 0 \n", - "8 0 0 0 \n", - "9 0 0 0 \n", - "10 0 0 0 \n", - "11 0 0 0 \n", - "\n", - " scale_coeff_ridge_alpha_d ... gamma_corrected alpha_d init_beta_naught \\\n", - "0 0.001 ... False True 0.0 \n", - "1 0.001 ... False True 0.0 \n", - "2 0.001 ... False True 0.0 \n", - "3 0.001 ... False True 0.0 \n", - "4 0.001 ... False True 0.0 \n", - "5 0.001 ... False True 0.0 \n", - "6 0.001 ... False True 0.0 \n", - "7 0.001 ... False True 0.0 \n", - "8 0.001 ... False True 0.0 \n", - "9 0.001 ... False True 0.0 \n", - "10 0.001 ... False True 0.0 \n", - "11 0.001 ... False True 0.0 \n", - "\n", - " lock_beta_naught_at tol num_training_steps iterations_per_step \\\n", - "0 None 0.0001 1 15000 \n", - "1 None 0.0001 1 15000 \n", - "2 None 0.0001 1 15000 \n", - "3 None 0.0001 1 15000 \n", - "4 None 0.0001 1 15000 \n", - "5 None 0.0001 1 15000 \n", - "6 None 0.0001 1 15000 \n", - "7 None 0.0001 1 15000 \n", - "8 None 0.0001 1 15000 \n", - "9 None 0.0001 1 15000 \n", - "10 None 0.0001 1 15000 \n", - "11 None 0.0001 1 15000 \n", - "\n", - " n_hidden_units lower_bound PRNGKey \n", - "0 5 -3.5 0 \n", - "1 5 -3.5 0 \n", - "2 5 -3.5 0 \n", - "3 5 -3.5 0 \n", - "4 5 -3.5 0 \n", - "5 5 -3.5 0 \n", - "6 5 -3.5 0 \n", - "7 5 -3.5 0 \n", - "8 5 -3.5 0 \n", - "9 5 -3.5 0 \n", - "10 5 -3.5 0 \n", - "11 5 -3.5 0 \n", - "\n", - "[12 rows x 21 columns]" + " epistatic_model output_activation init_theta_scale init_theta_bias \\\n", + "0 Sigmoid Softplus 6.5 -3.5 \n", + "1 Sigmoid Softplus 6.5 -3.5 \n", + "2 Sigmoid Softplus 6.5 -3.5 \n", + "3 Sigmoid Softplus 6.5 -3.5 \n", + "4 Sigmoid Softplus 6.5 -3.5 \n", + "5 Sigmoid Softplus 6.5 -3.5 \n", + "6 Sigmoid Softplus 6.5 -3.5 \n", + "7 Sigmoid Softplus 6.5 -3.5 \n", + "8 Sigmoid Softplus 6.5 -3.5 \n", + "9 Sigmoid Softplus 6.5 -3.5 \n", + "10 Sigmoid Softplus 6.5 -3.5 \n", + "11 Sigmoid Softplus 6.5 -3.5 \n", + "\n", + " n_hidden_units lower_bound PRNGKey maxiter scale_coeff_lasso_shift \\\n", + "0 5 -3.5 0 1000 0.0 \n", + "1 5 -3.5 0 1000 0.000001 \n", + "2 5 -3.5 0 1000 0.00001 \n", + "3 5 -3.5 0 1000 0.00005 \n", + "4 5 -3.5 0 1000 0.0001 \n", + "5 5 -3.5 0 1000 0.001 \n", + "6 5 -3.5 0 1000 0.0 \n", + "7 5 -3.5 0 1000 0.000001 \n", + "8 5 -3.5 0 1000 0.00001 \n", + "9 5 -3.5 0 1000 0.00005 \n", + "10 5 -3.5 0 1000 0.0001 \n", + "11 5 -3.5 0 1000 0.001 \n", + "\n", + " scale_coeff_ridge_beta scale_coeff_ridge_ge_scale dataset_name \\\n", + "0 0.000001 0.001 Replicate 1 \n", + "1 0.000001 0.001 Replicate 1 \n", + "2 0.000001 0.001 Replicate 1 \n", + "3 0.000001 0.001 Replicate 1 \n", + "4 0.000001 0.001 Replicate 1 \n", + "5 0.000001 0.001 Replicate 1 \n", + "6 0.000001 0.001 Replicate 2 \n", + "7 0.000001 0.001 Replicate 2 \n", + "8 0.000001 0.001 Replicate 2 \n", + "9 0.000001 0.001 Replicate 2 \n", + "10 0.000001 0.001 Replicate 2 \n", + "11 0.000001 0.001 Replicate 2 \n", + "\n", + " model fit_time \n", + "0 Model\\nName: unnamed\\nData: Replicate 1\\nConve... 109 \n", + "1 Model\\nName: unnamed\\nData: Replicate 1\\nConve... 108 \n", + "2 Model\\nName: unnamed\\nData: Replicate 1\\nConve... 108 \n", + "3 Model\\nName: unnamed\\nData: Replicate 1\\nConve... 108 \n", + "4 Model\\nName: unnamed\\nData: Replicate 1\\nConve... 109 \n", + "5 Model\\nName: unnamed\\nData: Replicate 1\\nConve... 107 \n", + "6 Model\\nName: unnamed\\nData: Replicate 2\\nConve... 106 \n", + "7 Model\\nName: unnamed\\nData: Replicate 2\\nConve... 105 \n", + "8 Model\\nName: unnamed\\nData: Replicate 2\\nConve... 105 \n", + "9 Model\\nName: unnamed\\nData: Replicate 2\\nConve... 107 \n", + "10 Model\\nName: unnamed\\nData: Replicate 2\\nConve... 106 \n", + "11 Model\\nName: unnamed\\nData: Replicate 2\\nConve... 106 " ] }, - "execution_count": 36, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -2866,7 +2999,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 51, "id": "1e78a46e", "metadata": {}, "outputs": [], @@ -2884,7 +3017,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 52, "id": "91907a87", "metadata": {}, "outputs": [ @@ -2918,15 +3051,17 @@ " \n", " \n", " mutation\n", - " beta\n", - " shift_Omicron_BA.1\n", - " shift_Omicron_BA.2\n", - " predicted_func_score_Delta\n", - " predicted_func_score_Omicron_BA.1\n", - " predicted_func_score_Omicron_BA.2\n", " times_seen_Delta\n", - " times_seen_Omicron_BA.1\n", - " times_seen_Omicron_BA.2\n", + " times_seen_Omicron_BA1\n", + " times_seen_Omicron_BA2\n", + " beta_Delta\n", + " beta_Omicron_BA1\n", + " shift_Omicron_BA1\n", + " beta_Omicron_BA2\n", + " shift_Omicron_BA2\n", + " predicted_func_score_Delta\n", + " predicted_func_score_Omicron_BA1\n", + " predicted_func_score_Omicron_BA2\n", " \n", " \n", " dataset_name\n", @@ -2941,6 +3076,8 @@ " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -2948,139 +3085,149 @@ " Replicate 1\n", " 0.0\n", " A1015D\n", - " -4.263447\n", - " -4.168998\n", - " -0.246434\n", - " -1.940598\n", - " -3.193376\n", - " -2.054570\n", " 5.0\n", " 2.0\n", " 3.0\n", + " -1.199526\n", + " -1.955241\n", + " -0.755714\n", + " -0.851112\n", + " 0.348414\n", + " -1.554889\n", + " -2.520092\n", + " -1.083576\n", " \n", " \n", " 0.0\n", " A1015Q\n", - " -6.989111\n", - " -3.998795\n", - " 0.000000\n", - " -3.151047\n", - " -3.223423\n", - " -3.069028\n", " 0.0\n", " 8.0\n", " 0.0\n", + " -0.048907\n", + " -2.681171\n", + " -2.632264\n", + " -0.000320\n", + " 0.048587\n", + " -0.053516\n", + " -3.140728\n", + " -0.000353\n", " \n", " \n", " 0.0\n", " A1015S\n", - " -0.019358\n", - " -1.080333\n", - " -0.496059\n", - " -0.001317\n", - " -0.068060\n", - " 0.037278\n", " 8.0\n", " 22.0\n", " 29.0\n", + " 0.179238\n", + " -0.256491\n", + " -0.435729\n", + " -0.196018\n", + " -0.375256\n", + " 0.185579\n", + " -0.303580\n", + " -0.225457\n", " \n", " \n", " 0.0\n", " A1015T\n", - " -9.385324\n", - " -1.990580\n", - " 1.863133\n", - " -3.275185\n", - " -3.224236\n", - " -3.124743\n", " 7.0\n", " 12.0\n", " 22.0\n", + " -1.782984\n", + " -1.990034\n", + " -0.207050\n", + " -1.834713\n", + " -0.051728\n", + " -2.318638\n", + " -2.557530\n", + " -2.383881\n", " \n", " \n", " 0.0\n", " A1015V\n", - " -4.685401\n", - " -4.448816\n", - " -1.000862\n", - " -2.263942\n", - " -3.209804\n", - " -2.743822\n", " 0.0\n", " 6.0\n", " 7.0\n", + " -0.080035\n", + " -2.431665\n", + " -2.351630\n", + " -1.975145\n", + " -1.895110\n", + " -0.088201\n", + " -2.972883\n", + " -2.542748\n", " \n", " \n", "\n", "" ], "text/plain": [ - " mutation beta shift_Omicron_BA.1 \\\n", - "dataset_name scale_coeff_lasso_shift \n", - "Replicate 1 0.0 A1015D -4.263447 -4.168998 \n", - " 0.0 A1015Q -6.989111 -3.998795 \n", - " 0.0 A1015S -0.019358 -1.080333 \n", - " 0.0 A1015T -9.385324 -1.990580 \n", - " 0.0 A1015V -4.685401 -4.448816 \n", - "\n", - " shift_Omicron_BA.2 \\\n", - "dataset_name scale_coeff_lasso_shift \n", - "Replicate 1 0.0 -0.246434 \n", - " 0.0 0.000000 \n", - " 0.0 -0.496059 \n", - " 0.0 1.863133 \n", - " 0.0 -1.000862 \n", + " mutation times_seen_Delta \\\n", + "dataset_name scale_coeff_lasso_shift \n", + "Replicate 1 0.0 A1015D 5.0 \n", + " 0.0 A1015Q 0.0 \n", + " 0.0 A1015S 8.0 \n", + " 0.0 A1015T 7.0 \n", + " 0.0 A1015V 0.0 \n", + "\n", + " times_seen_Omicron_BA1 \\\n", + "dataset_name scale_coeff_lasso_shift \n", + "Replicate 1 0.0 2.0 \n", + " 0.0 8.0 \n", + " 0.0 22.0 \n", + " 0.0 12.0 \n", + " 0.0 6.0 \n", + "\n", + " times_seen_Omicron_BA2 beta_Delta \\\n", + "dataset_name scale_coeff_lasso_shift \n", + "Replicate 1 0.0 3.0 -1.199526 \n", + " 0.0 0.0 -0.048907 \n", + " 0.0 29.0 0.179238 \n", + " 0.0 22.0 -1.782984 \n", + " 0.0 7.0 -0.080035 \n", + "\n", + " beta_Omicron_BA1 shift_Omicron_BA1 \\\n", + "dataset_name scale_coeff_lasso_shift \n", + "Replicate 1 0.0 -1.955241 -0.755714 \n", + " 0.0 -2.681171 -2.632264 \n", + " 0.0 -0.256491 -0.435729 \n", + " 0.0 -1.990034 -0.207050 \n", + " 0.0 -2.431665 -2.351630 \n", + "\n", + " beta_Omicron_BA2 shift_Omicron_BA2 \\\n", + "dataset_name scale_coeff_lasso_shift \n", + "Replicate 1 0.0 -0.851112 0.348414 \n", + " 0.0 -0.000320 0.048587 \n", + " 0.0 -0.196018 -0.375256 \n", + " 0.0 -1.834713 -0.051728 \n", + " 0.0 -1.975145 -1.895110 \n", "\n", " predicted_func_score_Delta \\\n", "dataset_name scale_coeff_lasso_shift \n", - "Replicate 1 0.0 -1.940598 \n", - " 0.0 -3.151047 \n", - " 0.0 -0.001317 \n", - " 0.0 -3.275185 \n", - " 0.0 -2.263942 \n", - "\n", - " predicted_func_score_Omicron_BA.1 \\\n", - "dataset_name scale_coeff_lasso_shift \n", - "Replicate 1 0.0 -3.193376 \n", - " 0.0 -3.223423 \n", - " 0.0 -0.068060 \n", - " 0.0 -3.224236 \n", - " 0.0 -3.209804 \n", - "\n", - " predicted_func_score_Omicron_BA.2 \\\n", - "dataset_name scale_coeff_lasso_shift \n", - "Replicate 1 0.0 -2.054570 \n", - " 0.0 -3.069028 \n", - " 0.0 0.037278 \n", - " 0.0 -3.124743 \n", - " 0.0 -2.743822 \n", - "\n", - " times_seen_Delta \\\n", - "dataset_name scale_coeff_lasso_shift \n", - "Replicate 1 0.0 5.0 \n", - " 0.0 0.0 \n", - " 0.0 8.0 \n", - " 0.0 7.0 \n", - " 0.0 0.0 \n", - "\n", - " times_seen_Omicron_BA.1 \\\n", - "dataset_name scale_coeff_lasso_shift \n", - "Replicate 1 0.0 2.0 \n", - " 0.0 8.0 \n", - " 0.0 22.0 \n", - " 0.0 12.0 \n", - " 0.0 6.0 \n", - "\n", - " times_seen_Omicron_BA.2 \n", - "dataset_name scale_coeff_lasso_shift \n", - "Replicate 1 0.0 3.0 \n", - " 0.0 0.0 \n", - " 0.0 29.0 \n", - " 0.0 22.0 \n", - " 0.0 7.0 " + "Replicate 1 0.0 -1.554889 \n", + " 0.0 -0.053516 \n", + " 0.0 0.185579 \n", + " 0.0 -2.318638 \n", + " 0.0 -0.088201 \n", + "\n", + " predicted_func_score_Omicron_BA1 \\\n", + "dataset_name scale_coeff_lasso_shift \n", + "Replicate 1 0.0 -2.520092 \n", + " 0.0 -3.140728 \n", + " 0.0 -0.303580 \n", + " 0.0 -2.557530 \n", + " 0.0 -2.972883 \n", + "\n", + " predicted_func_score_Omicron_BA2 \n", + "dataset_name scale_coeff_lasso_shift \n", + "Replicate 1 0.0 -1.083576 \n", + " 0.0 -0.000353 \n", + " 0.0 -0.225457 \n", + " 0.0 -2.383881 \n", + " 0.0 -2.542748 " ] }, - "execution_count": 38, + "execution_count": 52, "metadata": {}, "output_type": "execute_result" } @@ -3108,7 +3255,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 53, "id": "8b09dd63", "metadata": {}, "outputs": [ @@ -3124,23 +3271,23 @@ "text/html": [ "\n", "\n", - "
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\n", "" ], "text/plain": [ "alt.FacetChart(...)" ] }, - "execution_count": 42, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } diff --git a/pyproject.toml b/pyproject.toml index 3a4852d..03e97dc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,7 +22,8 @@ classifiers = [ "Programming Language :: Python :: 3.9", ] keywords = [ - "multidms", + "multidms", + "generalized lasso", "deep", "mutational", "scanning", @@ -36,10 +37,10 @@ keywords = [ requires-python = ">=3.9" dependencies = [ "polyclonal", - "jax[cpu]==0.4.24", + "jax[cpu]>=0.4.29", "jaxopt", "typing_extensions", - "numpy", + "numpy<=1.26.0", "pandas>=2.2.0", "binarymap", "altair==5.1.2", # pandas convert_dtypes bug diff --git a/tests/test_data.py b/tests/test_data.py index 143a37f..7e39f1e 100644 --- a/tests/test_data.py +++ b/tests/test_data.py @@ -205,6 +205,19 @@ def test_single_mut_encodings(): ) +def test_plotting_fxns(): + """Test that the plotting functions work""" + Data = multidms.Data( + TEST_FUNC_SCORES, + alphabet=multidms.AAS_WITHSTOP, + reference="a", + assert_site_integrity=False, + ) + + Data.plot_times_seen_hist(show=False) + Data.plot_func_score_boxplot(show=False) + + r""" +++++++++++++++++++++++++++++ UTILS @@ -270,6 +283,13 @@ def test_linear_model_fit_simple(): model = multidms.Model(data, multidms.biophysical.identity_activation, PRNGKey=23) model.fit(maxiter=2, warn_unconverged=False) + # test all plotting fxn's + model.plot_pred_accuracy(show=False) + model.plot_epistasis(show=False) + model.plot_param_hist("beta_a", show=False) + model.plot_param_heatmap("beta_a", show=False) + _ = model.mut_param_heatmap("beta") + def test_linear_model_multi_cond_fit_simple(): """ @@ -283,9 +303,16 @@ def test_linear_model_multi_cond_fit_simple(): assert_site_integrity=False, ) model = multidms.Model(data, multidms.biophysical.identity_activation, PRNGKey=23) - model.fit(maxiter=2, warn_unconverged=False) + # test all plotting fxn's + model.plot_pred_accuracy(show=False) + model.plot_epistasis(show=False) + model.plot_param_hist("shift_b", show=False) + model.plot_param_heatmap("shift_b", show=False) + model.plot_shifts_by_site("b", show=False) + _ = model.mut_param_heatmap("shift") + def test_fit_simple(): """ @@ -303,6 +330,13 @@ def test_fit_simple(): model.fit(maxiter=2, warn_unconverged=False) assert loss != model.loss + # test all plotting fxn's + model.plot_pred_accuracy(show=False) + model.plot_epistasis(show=False) + model.plot_param_hist("beta_a", show=False) + model.plot_param_heatmap("beta_a", show=False) + _ = model.mut_param_heatmap("beta") + def test_multi_cond_fit_simple(): """ @@ -316,9 +350,16 @@ def test_multi_cond_fit_simple(): assert_site_integrity=False, ) model = multidms.Model(data, PRNGKey=23) - model.fit(maxiter=2, warn_unconverged=False) + # test all plotting fxn's + model.plot_pred_accuracy(show=False) + model.plot_epistasis(show=False) + model.plot_param_hist("shift_b", show=False) + model.plot_param_heatmap("shift_b", show=False) + model.plot_shifts_by_site("b", show=False) + _ = model.mut_param_heatmap() + def test_scaled_predictions(): """