class Autocoder[source]

Autocoder(verbose=1, device=None)

Autocodes text fields

Autocoder.code_sentiment[source]

Autocoder.code_sentiment(docs, df, batch_size=8, binarize=False, threshold=0.5)

Autocodes text for positive or negative sentiment

Let's prepare a toy dataset:

ac = Autocoder()
reviews = ["I loved this doctor!", "This doctor was absolutely terrible."]
df = pd.DataFrame({
    'gender': ['female', 'male'],
     'review' : reviews,
      })
df.head()

	gender	review
0	female	I loved this doctor!
1	male	This doctor was absolutely terrible.

After autocoding for sentiment, the dataframe now has extra columns:

result_df = ac.code_sentiment(df['review'].values, df)
result_df.head()

	gender	review	negative	positive
0	female	I loved this doctor!	0.005034	0.994966
1	male	This doctor was absolutely terrible.	0.981789	0.018211

assert result_df[result_df['gender']=='female']['negative'].values[0] < 0.1
assert result_df[result_df['gender']=='female']['positive'].values[0] > 0.9
assert result_df[result_df['gender']=='male']['negative'].values[0] > 0.9
assert result_df[result_df['gender']=='male']['positive'].values[0] < 0.1

Autocoder.code_custom_topics[source]

Autocoder.code_custom_topics(docs, df, labels, batch_size=8, binarize=False, threshold=0.5)

Autocodes text for user-specified topics. The label field is the name of the topic as a string (or a list of them.)

Let's prepare a toy dataset:

comments = ["What is your favorite sitcom of all time?", 'I cannot wait to vote!']
df = pd.DataFrame({
    'over_18': ['yes', 'no'],
     'comments' : comments,
      })
df.head()

	over_18	comments
0	yes	What is your favorite sitcom of all time?
1	no	I cannot wait to vote!

After autocoding, the dataframe has a new column for each custom topic:

result_df = ac.code_custom_topics(df['comments'].values, df, labels=['television', 'film', 'politics'])
result_df.head()

	over_18	comments	television	film	politics
0	yes	What is your favorite sitcom of all time?	0.981327	0.012260	0.000157
1	no	I cannot wait to vote!	0.000518	0.004943	0.936988

assert result_df[result_df['over_18']=='yes']['television'].values[0] > 0.9
assert result_df[result_df['over_18']=='yes']['film'].values[0] < 0.1
assert result_df[result_df['over_18']=='yes']['politics'].values[0] < 0.1
assert result_df[result_df['over_18']=='no']['television'].values[0] < 0.1
assert result_df[result_df['over_18']=='no']['film'].values[0] < 0.1
assert result_df[result_df['over_18']=='no']['politics'].values[0] > 0.9

Autocoder.code_emotion[source]

Autocoder.code_emotion(docs, df, batch_size=8, binarize=False, threshold=0.5)

Autocodes text for emotion

comments = ["I'm nervous about tomorrow.", 'I got a promotion at work!',
            "My best friend was in a car accident.", "I hate it when I'm cut off in traffic."]
df = pd.DataFrame({
    'over_18': ['yes', 'no', 'yes', 'yes'],
     'comments' : comments,
      })
df.head()

	over_18	comments
0	yes	I'm nervous about tomorrow.
1	no	I got a promotion at work!
2	yes	My best friend was in a car accident.
3	yes	I hate it when I'm cut off in traffic.

result_df = ac.code_emotion(df['comments'].values, df, binarize=True)
result_df.head()

	over_18	comments	joy	anger	fear	sadness
0	yes	I'm nervous about tomorrow.	0	0	1	0
1	no	I got a promotion at work!	1	0	0	0
2	yes	My best friend was in a car accident.	0	0	0	1
3	yes	I hate it when I'm cut off in traffic.	0	1	0	0

assert result_df.iloc[0]['fear'] == 1
assert result_df.iloc[1]['joy'] == 1
assert result_df.iloc[2]['sadness'] == 1
assert result_df.iloc[3]['anger'] == 1

Autocoder.code_transformer[source]

Autocoder.code_transformer(docs, df, batch_size=32, model_name='stsb-roberta-large', show_progress_bar=False)

Encode texts as semantically meaningful vectors using a Transformer model

reviews = ["I loved this doctor!", "This doctor was absolutely terrible."]
df = pd.DataFrame({
    'gender': ['female', 'male'],
     'review' : reviews,
      })
df.head()

	gender	review
0	female	I loved this doctor!
1	male	This doctor was absolutely terrible.

df = ac.code_transformer(df.review.values, df)

df.head()

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0	female	I loved this doctor!	-0.601180	0.639239	-1.060369	-0.493731	-0.560601	-1.008939	-0.598373	-0.672984	-0.640709	0.035109	-0.394858	1.125174	-0.809709	0.092503	-1.561161	-0.338891	-0.980971	-0.218150	-0.770218	0.518710	-0.154178	-0.465516	-0.636097	0.136777	-0.671058	0.887400	1.150700	-0.255780	-0.124600	-1.695019	-0.176871	-0.554525	0.420271	1.104315	-0.662254	-1.104489	-0.150348	-0.328107	-0.265295	-0.232560	-0.732200	0.102851	1.920283	0.345062	0.727855	-0.558262	-0.727879	0.068228	-0.288561	-1.376903	0.480348	-0.951236	-0.184960	-0.977992	-0.494253	-0.142820	0.186124	0.165433	-0.054685	0.401775	-0.606251	-0.400375	0.273657	-0.347373	0.430465	0.691614	-0.515043	-0.089149	0.224054	-0.449324	-0.194017	0.594868	-0.614699	-0.372429	-0.152741	-0.066052	1.074707	-0.810009	0.675266	-0.609482	0.561731	-0.939348	-0.691044	-0.995084	0.166328	-1.531809	-0.379524	-0.498860	-0.741533	-0.413629	1.733109	-0.791184	-0.098716	-1.233320	0.137790	0.938824	0.544055	-1.024858	0.578154	-0.508842	-1.023441	0.597845	1.085201	-1.700814	-0.930898	0.512371	-1.246665	-0.310088	0.550669	-1.052263	0.829993	-0.637790	-0.438172	-0.568537	0.722001	-0.957278	-0.768909	-0.160705	1.836634	-0.581477	0.488977	0.347504	0.783655	0.589048	-0.770469	0.439723	-0.408767	0.295209	1.149268	0.160561	0.342767	-1.275258	-0.075461	0.347347	-1.197512	-1.346758	0.052439	-1.996378	0.061255	-0.809439	-0.636264	-0.521608	0.209666	1.201379	1.304154	0.858928	1.373042	0.723125	-0.444027	0.397904	-1.185389	0.309025	0.101140	0.790087	-0.622007	-0.557396	-1.449296	-0.310137	1.294056	0.66767	-1.077920	-0.054805	-0.571364	1.299067	-0.331780	-0.840044	1.282067	0.425645	1.46890	-0.662942	0.312071	1.420856	0.084983	0.438224	-0.310173	-0.981818	0.668649	-1.796632	-0.476523	0.171581	0.08128	-1.055869	0.731145	0.082770	0.402360	-0.111507	1.052606	0.101429	-0.436716	-0.689745	-0.359305	-0.849818	0.102386	-0.674699	-0.632386	0.635284	-0.454286	0.002086	-0.698927	-1.261298	0.795101	-0.073547	-0.325837	0.421853	-1.620993	1.901134	-0.371985	-1.075006	0.779401	-0.981726	1.718573	-0.156533	-1.501477	0.638842	-0.603821	-0.441458	-0.419934	1.299583	-0.329041	0.187053	1.476716	0.841890	1.378884	1.415993	0.490228	0.93683	-1.134727	-1.298774	-0.237284	-0.639338	-0.062777	-0.571427	-0.696611	-1.674279	0.200118	0.566758	1.258007	0.281263	-0.227386	0.403024	-0.913720	-0.332624	-1.145163	-1.373416	0.726468	-0.116224	-1.080073	1.629549	...	-0.597258	0.473389	-0.087902	-0.734512	-0.192177	1.098324	0.252797	-0.220380	0.970834	0.379641	0.702579	0.312840	-0.014865	0.076790	-0.926711	0.283459	-0.201210	-1.507544	1.013160	0.399853	-0.560346	-0.432460	0.738794	0.271019	0.758012	0.104948	0.032012	-1.118263	0.817341	-0.134954	-0.367428	-1.095511	1.424716	-0.45837	-1.005259	1.168612	-0.739624	-0.778042	-0.356735	0.470458	0.181306	0.867469	-0.033199	-0.059742	0.067898	-0.396584	1.678158	-0.886795	0.431772	0.239491	-0.398206	0.357574	-0.649486	0.884956	0.774565	-0.091967	0.539807	-0.098839	0.407467	0.022493	0.596556	-2.279631	-1.012586	-0.515414	1.008494	0.024449	0.786387	-0.039095	-0.282467	1.210615	0.009027	0.694995	-0.778203	-0.434733	-0.546121	0.111783	-0.414437	-0.186292	-0.924311	0.771270	-0.726940	-0.002945	-0.904097	-0.78010	-1.344393	0.419025	0.236579	-0.147506	0.422931	0.268999	-1.120625	-2.346339	0.059263	0.432407	-0.029169	0.342242	-0.227718	0.429898	-0.487460	0.215381	-1.755592	0.571806	1.145492	-0.595226	0.279368	-1.833523	-0.318555	-0.334240	1.546089	0.996179	0.365355	0.795756	0.931366	-1.328836	2.221819	0.533793	0.419647	0.607096	1.148281	0.962832	-0.627507	0.023852	-0.977026	0.372186	-0.191951	-0.261494	1.279736	0.743437	0.312943	0.249434	-1.020184	-0.526093	-0.145118	-1.224916	0.013893	0.314860	-0.184937	-0.325164	1.366373	0.274657	0.026925	-0.244764	-0.087459	2.440723	-0.211444	1.791491	-1.783760	1.172868	-1.588579	0.547428	1.236403	0.238765	1.074080	0.971804	1.481358	-0.260144	-0.372862	-1.668835	0.814127	0.459048	-0.537239	-1.363500	-1.937048	0.223611	-0.093947	0.206138	1.323856	-0.881426	0.858833	-0.481818	-1.63406	1.143431	-0.822667	-0.389236	0.754676	-0.474368	1.164978	-1.249432	0.841197	-0.271101	0.239336	-0.874708	-0.484608	1.776312	-0.655398	-0.595401	1.292877	-0.673088	1.183725	1.045448	-0.711501	-0.435948	-0.414408	-0.82087	0.125983	0.092412	0.571426	-1.369650	0.498595	-0.114022	-2.056757	-0.606038	-0.014727	-1.732948	-0.208160	-0.257968	0.336272	0.292738	-1.020895	0.707942	-0.413066	0.015892	-0.870656	0.356665	-1.240625	0.697207	-0.899096	-0.546283	1.346067	0.151549	0.608179	-0.642331	-0.491367	1.476060	-0.239341	0.210075	0.653871	0.124511	-1.450796	0.131711	0.597644	-0.239655	0.151939	-0.989297	1.120132	0.086377	0.172451	-1.515352	-0.422561	1.618894	1.162732	-0.041656	-0.473772	0.420647	-0.482861	0.206311	-0.806356	0.864795	-0.179643	-0.095540
1	male	This doctor was absolutely terrible.	-1.080321	1.283710	0.032944	-0.505388	-0.632284	0.240779	0.497700	0.061434	-0.951467	-1.099914	0.371787	1.267668	-0.751966	-0.042724	-0.142016	0.127234	-0.733424	-1.139796	-0.325070	0.430322	-0.098004	1.163077	1.057190	0.532064	-0.054028	-0.344783	1.042196	0.132536	0.173455	-0.846880	-0.294927	-1.092173	-0.739157	0.072505	-1.381498	-0.039768	-0.596037	-0.635421	-0.102166	-0.223891	-0.110668	1.610051	0.124495	0.262522	0.471182	0.363986	0.149284	1.757610	-0.095173	-0.828335	-0.169187	-0.167354	0.181549	-0.468074	0.173165	-0.151472	0.153541	-0.070349	-0.070682	1.346813	0.838431	-0.173599	-0.698330	-0.907078	0.686929	-0.253123	-0.253507	-0.816285	0.577228	-0.471222	-0.319503	0.318208	-1.152313	1.608094	0.020386	0.240881	1.051513	-0.431564	-0.734053	0.355924	-0.735063	-1.024491	-0.607373	-0.363772	-1.032262	-0.755497	-1.072544	-0.330346	0.112159	-0.765853	2.702498	-0.059790	-2.331072	-0.261409	0.662297	-0.134803	2.094935	-1.216020	-1.468843	-0.590109	-0.603379	0.032229	-0.734086	-1.041735	-0.096881	0.252744	-0.755398	-0.196471	-0.673408	0.323116	0.485170	0.852233	0.038043	0.106503	1.900742	-0.473968	0.440853	-0.124218	0.818130	-0.249900	0.174284	-2.027710	-0.841279	-0.510334	-1.589421	-0.064431	-0.204134	0.107323	-0.129780	-0.373625	-0.085754	-0.389158	0.630451	0.811590	-1.157425	-0.036667	0.638930	-0.031828	0.162673	-0.745701	0.047340	0.041956	0.455531	1.466353	-0.493203	0.315198	0.956463	0.169743	-0.903740	1.078133	-0.639152	0.206805	-1.212701	0.061930	-1.587089	0.509692	-0.580704	0.743137	0.439220	0.11038	-1.247447	1.323940	0.404472	0.451868	-1.951448	-2.136478	-0.824689	0.520747	0.87729	-0.365677	0.608508	1.291322	0.141776	0.668782	0.493870	-0.911925	-0.265987	-0.342515	0.059859	-0.457266	-0.24478	1.999361	-0.012580	0.126561	-0.443919	1.152566	-0.219918	-0.358424	-0.215555	0.169946	0.193413	0.425413	0.506095	-2.375514	-0.682047	-0.212779	0.261091	-0.382527	-0.423046	0.087569	0.485063	-0.342660	0.455986	0.331639	-1.648497	1.399007	-0.594800	0.471352	-0.741982	0.568690	-0.537344	1.354499	-1.521543	0.222686	0.505541	-0.384466	0.048947	0.243410	-1.003186	0.442602	1.256965	0.718853	1.458385	1.336809	-1.110115	-0.28113	-0.021441	0.969155	-0.324079	-0.551153	-0.346971	-0.426813	-0.909856	-0.224591	0.519270	0.436378	0.557002	0.615946	0.307261	-0.292611	-0.646692	-0.091192	-0.124168	0.044792	0.370954	-1.421038	-1.321087	1.192953	...	-0.160493	-1.280425	-0.769862	0.573256	-1.297933	1.492451	1.244544	0.312218	-0.620741	0.367966	2.416998	2.586343	-1.135545	0.896954	0.391467	-0.674775	0.383277	-0.950578	1.830727	-1.018144	-0.007086	-0.491024	0.520239	0.675352	1.206401	-1.113754	-1.293386	-0.928670	0.735877	0.426821	-0.453119	-0.505470	0.643926	-0.40995	-1.265347	-0.086370	0.149850	-0.014541	0.152579	0.214134	0.190900	0.483520	-0.121119	0.216187	-0.095705	0.484240	-0.256438	0.128706	0.124124	0.442363	-0.328852	0.839022	-0.413680	-0.218301	0.031112	-0.781577	0.877376	0.426151	0.650736	-0.534363	1.324010	-2.276321	-3.209808	0.747673	-0.090331	-0.794744	0.910227	0.064211	0.187118	-0.292773	-0.751870	0.891957	-0.681515	-1.061648	-0.573387	0.548157	-0.167158	-0.570218	-0.115314	0.747868	-0.937214	-0.019237	-1.126545	-0.36322	-1.234232	0.423862	-0.269932	0.576194	0.849581	0.444871	-0.502688	-1.018462	-0.920363	-0.202659	-0.456458	1.216924	-0.185181	0.486069	0.267084	0.585335	0.036500	-0.048680	1.431088	-0.141862	0.566101	-1.238389	0.072949	0.038206	-0.293941	1.536463	0.458766	-0.149625	-0.717818	-0.079780	1.701869	0.439535	-0.174674	0.958559	-0.054750	0.944752	-0.018844	0.701800	-0.769989	0.253060	0.769639	-0.607609	0.696354	0.171143	1.106053	0.268299	-1.047965	0.640154	0.143615	-1.105975	-0.016227	0.142468	0.596629	-0.452742	-0.313863	-0.227832	-0.207953	-0.843668	-1.502774	1.050109	-0.042179	0.633935	-0.994892	-0.309290	-1.750694	-1.035756	-0.893423	0.439106	0.468417	0.332214	0.615565	0.167857	-0.761188	-0.513775	-0.727299	0.233110	0.549183	-1.956708	-0.498497	-0.176335	-1.125636	-0.663086	-0.504846	-0.284807	1.412328	1.304304	-0.67363	1.146111	-1.070053	-0.598915	0.518672	-0.419871	-0.001672	-0.915121	1.048180	1.200090	-1.123845	-0.956011	-0.779801	1.226384	0.299932	0.497791	-0.184537	-0.028379	0.185598	0.613601	-0.006552	-0.340542	0.135926	-0.15309	-0.933908	-0.327588	1.260057	-0.727343	-0.019971	0.352552	-0.667697	-1.120148	-0.257728	0.343014	0.514783	-1.494829	-0.767745	-0.098165	-0.532586	1.300745	0.445362	-0.591072	0.472784	0.128228	-0.951936	-0.301227	-0.829075	0.356493	2.177831	-0.453740	0.180738	-0.366111	0.788271	-0.376016	-0.167796	0.945092	0.318102	-0.313438	-0.521864	-0.804645	0.371298	-0.102799	-0.398658	-0.674932	0.712733	0.402257	-0.189253	-1.744041	0.592453	-0.101446	1.562682	-0.446034	-0.073316	0.778162	-0.670258	0.576500	-0.036422	-0.237191	-0.103962	-0.018753

2 rows × 1026 columns

Autocoder.code_lda_topics[source]

Autocoder.code_lda_topics(docs, df, k=10, n_features=10000)

Encode texts as semantically meaningful vectors using Latent Dirichlet Alocation

comments = ["What is your favorite sitcom of all time?", 'I cannot wait to vote!']
df = pd.DataFrame({
    'over_18': ['yes', 'no'] * 5,
     'comments' : comments * 5,
      })
df.head()

	over_18	comments
0	yes	What is your favorite sitcom of all time?
1	no	I cannot wait to vote!
2	yes	What is your favorite sitcom of all time?
3	no	I cannot wait to vote!
4	yes	What is your favorite sitcom of all time?

df = ac.code_lda_topics(df['comments'].values, df)

preprocessing texts...
fitting model...
iteration: 1 of max_iter: 5
iteration: 2 of max_iter: 5
iteration: 3 of max_iter: 5
iteration: 4 of max_iter: 5
iteration: 5 of max_iter: 5
done.
done.

df.head()

	over_18	comments	topic_0000	topic_0001	topic_0002	topic_0003	topic_0004	topic_0005	topic_0006	topic_0007	topic_0008	topic_0009
0	yes	What is your favorite sitcom of all time?	0.148763	0.093341	0.080723	0.128911	0.109816	0.084724	0.093611	0.080860	0.091758	0.087493
1	no	I cannot wait to vote!	0.085687	0.097749	0.142486	0.084145	0.086931	0.099608	0.091913	0.114741	0.093014	0.103728
2	yes	What is your favorite sitcom of all time?	0.148763	0.093341	0.080723	0.128911	0.109816	0.084724	0.093611	0.080860	0.091758	0.087493
3	no	I cannot wait to vote!	0.085687	0.097749	0.142486	0.084145	0.086931	0.099608	0.091913	0.114741	0.093014	0.103728
4	yes	What is your favorite sitcom of all time?	0.148763	0.093341	0.080723	0.128911	0.109816	0.084724	0.093611	0.080860	0.091758	0.087493

Autocoder.code_callable[source]

Autocoder.code_callable(docs, df, fn)

Autocodes text for any user-specified function The fn parameter must be a Callable and return a dictionary for each text in docs where the keys are desired column names and values are scores or probabilities.

reviews = ["I loved this doctor!", "This doctor was absolutely terrible."]
df = pd.DataFrame({
    'gender': ['female', 'male'],
     'review' : reviews,
      })
df.head()

	gender	review
0	female	I loved this doctor!
1	male	This doctor was absolutely terrible.

def some_function(x):
    val = int('terrible' in x)
    return {'has_the_word_terrible?' : val}

df = ac.code_callable(df.review.values, df, some_function)

df.head()

	gender	review	has_the_word_terrible?
0	female	I loved this doctor!	0
1	male	This doctor was absolutely terrible.	1